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A collaborative architecture of the industrial internet platform for manufacturing systems
Robotics and Computer Integrated Manufacturing 61 (2020) 101854
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Full length Article
A collaborative architecture of the industrial internet platform for manufacturing systems
T
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Wang Junlianga, Xu Chuqiaob, Zhang Jiea, , Bao Jingsonga, Zhong Rayc a
College of Mechanical Engineering, Donghua University, Shanghai, China School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China c Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong b
A R T I C LE I N FO
A B S T R A C T
Keywords: Industrial internet Fog computing Digital thread Micro-service
One of the most significant advances in the development of intelligent manufacturing is represented by the industrial Internet, which is combining the physical and cyber components in manufacturing systems together. Aiming to manage the interaction between the physical and cyber components, this paper proposes a collaborative architecture for industrial Internet platform (IIP) called industrial operation system (Ind-OS), which contains the industrial driver, digital thread and micro-services to provide a better cooperative enterprise information system (EIS) environment for manufacturing systems. The industrial driver in the edge layer is presented for each resource unit for communication, computation, control, identification, insight and interoperation, realizing a ``plug and play" fashion in IIP. The digital thread is designed to link all resource units and metadata, which consists of a digital resource chain and an integrated information chain. In the application layer, all the businesses in EIS are decoupled into different micro-services, and a “Jigsaw Apps” is recombined to support the operation of manufacturing systems. A case study illustrates the effectiveness of the proposed Ind-OS, and the impacts caused by the Ind-OS are discussed.
1. Introduction In the Industry 4.0 era, it has become a trend that fusing the physical and cyber components together gain insight into the industrial process from the data for improving productivity, efficiency and reliability [1,2]. The industrial Internet platforms (IIPs) manage the interaction between the physical and cyber components, which are the core in operating the industrial systems. Extensive works have been done with IIP in the past several decades [3,4], such as GE Predix, ABB Ability, Siemens MindSphere, PTC ThingWorx, etc. The current platforms extend the managing domain to cover all stages of a product life cycle. For example, Siemens developed an IIP called MindSphere to realize the interconnection of about 1 million devices and systems and provide predictive maintaining services for the devices [5]. The IIPs link the physical and cyber components together, capacitating the resource, data, and knowledge available everywhere in manufacturing systems, have emerged as a rising hotpot in the academic and industrial circle [6–8]. The current IIPs mainly concerned about the maintenance of intelligent products, and the research on operation of manufacturing systems is limited [9,10]. Nowadays, the operation of manufacturing
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systems are realized through various enterprise information systems (EIS) [11], including human resource management (HRM) systems [12], customer relationship management (CRM) systems [13], enterprise resource planning (ERP) systems [14], manufacturing executive system (MES) [15,16], etc. The collaboration between these EISs is difficult since they are usually developed by different companies in various periods, resulting in several information islands. While it is also important to note that an EIS should be collaborative with other systems to meet the rapidly changing market of an enterprise. For example, product designers will consider the customer's preference obtained from the CRM system [17]. The customer's data should link to the design data to support collaborative optimization. With the requirements of the initiative collaboration scheme, different EISs should be operated and connected together for an effective management framework of the enterprise systems. This is significant when the enterprises in the supply chain are integrating together to provide services for the customers. And this, in turn, will lead to the continuous evolution to a more cooperative information/knowledge-driven EIS environment [18]. The IIP integrates physical and cyber components together weakening the boundary of different EISs to build a reconfigurable and
Corresponding author. E-mail address: [email protected] (J. Zhang).
https://doi.org/10.1016/j.rcim.2019.101854 Received 25 April 2019; Received in revised form 8 August 2019; Accepted 16 August 2019 0736-5845/ © 2019 Published by Elsevier Ltd.
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to extract data from different datasets of EISs [34]. Sun et al. proposed a new data integration method. After converting reward-complement balanced data set into multiple balanced data sets, a number of classifiers are constructed by using specific classification algorithms, and the classification results are combined by specific integration rules [35]. Wen et al. proposed a new design and development of ETL system for marine environmental data cleaning, transformation and integration, and offered data access interfaces [36]. Chen, et al. improved the algorithm applied to large data integration learning, proposed a new parallel random forest (PRF) algorithm on the Apache Spark platform [37]. Di et al. proposes a new innovative design method, GrHyMM, which can integrate structured and unstructured heterogeneous raw data [38]. Andrzejak et al. proposed an efficient merging method of decision trees into a single decision tree, for parallel learning and learning from distributed data [39]. Peter Baumann et al. presented and discussed the main results of the Earth Server project and expound the analysis and application of earth big data [40]. Soltanpoor et al. proposed a descriptive, predictive and prescriptive big data analytics framework, which links the extracted insight from the data to the pertinent generated actions [41]. Wen et al. proposed a method to analyze data sets by using neural network modelling, which is related to data mining and fault diagnosis [42]. At the second stage of the industrial platform, the big data platform is now emerging to be one of the core technologies that underlie different kinds of EISs to achieve a better collaborative optimization ability through big data analytics [43,44]. However, in the Industry 4.0 era, manufacturing systems are facing new challenges in the ever-changing dynamic environment [45]. As a result, the big data platform only integrating different EISs together can hardly acclimatize to the fast-paced changing manufacturing systems.
ubiquitous service environment [19], which provides a good condition for end-to-end collaboration. The loose coupling structure is beneficial for system resilience [20,21], since the service and system structure can be adjusted according to dynamic events. This paper proposed an industrial operation system (Ind-OS) for manufacturing systems with the architecture of IIP to enable easier collaboration of EISs. The rest of this manuscript is organized as follows: Section 2 reviews the brief history of the industrial internet platform. Section 3 designs the referenced framework of the Ind-OS, including industrial driver for edge layer, digital thread for industrial PaaS layer, and the interoperable application layer. A case study is conducted in Section 4 to illustrate the effectiveness and feasibility of the proposed Ind-OS. The difference between the proposed Ind-OS and the current EIS architecture is discussed in Section 5. Finally, conclusions and recommendations for future work are summarized in Section 6. 2. Brief history 2.1. The first stage of the industrial internet platform The first generation of the IIP is essentially an industrial platform in SaaS (Software as a Service) model with several enterprise information systems (e.g. Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Supply Chain Management (SCM)) to support the operation of manufacturing systems. These EISs are developed toward cloud computing architecture and implemented on a PaaS platform (e.g. Windows Azure, Google App Engine, and Force.com) to provide operation service for enterprises [22]. Oppong et al. developed a workflow management system (WfMS) to improve the process efficiency by information availability improvement, process standardization, task assignment on an automatic basis to track process-related information and the status of each instance process [23]. Li et al. developed a customer relationship management (CRM) system oriented SaaS, which manages existing customers and explores further business opportunities with a cross-industry standard process for data mining (CRISP-DM) methodology [24]. Marcello et al. proposed a business process management (BPM) system on a SaaS platform to reduce the responding time of business tasks with changing environment [25]. To manage the different EISs on the SaaS platform, Li et al. presented a hybrid wireless network integration scheme to filter the suitable and available cloud services with service access requirements and user security credentials [26]. Usually, these EISs are developed by different suppliers with diverse software architectures in various periods. As a result, EISs in manufacturing systems are isolated with each other, which are well known as “isolated islands”. With the requirements of industrial data sharing and analysis, the SaaS model can hardly meet the fast development of EISs.
2.3. The third stage of the industrial internet platform The increasing connectivity provided by the Internet of Things (IoT) supports novel opportunities for the IIP to meld the physical industrial components and EISs into an integral whole. Many companies have proposed industrial internet platforms to seize the commanding heights of the new industrial revolution. The Industrial Internet Consortium (IIC) is the world's leading organization transforming business and society by accelerating the adoption of the Industrial Internet of Things (IIoT), and oneM2M, the global IoT standards initiative. As the leading company of the IIC, GE released Predix, an industrial Internet platform released by IIC's lead enterprise GE, has achieved access to more than 10 areas of equipment [46]. ABB set up a new digital teleservice center for energy-efficient inverters in Bangalore, which provided end-to-end remote access to frequency converters in end-user plants for predictive maintenance and condition monitoring [47]. The next-generation digital solutions and services of ABB would be developed and built on the Microsoft Azure cloud platform and worked with IBM Watson IoT cognitive computing to create real-time cognitive analysis capabilities in smart factory. Honeywell developed a predictive maintenance service named GoDirect for connected auxiliary power units (APUs), and Hainan Airlines became the first airline in the world to adopt GoDirect [48]. The interconnected APU service used the data connections already on the aircraft to download APU maintenance and fault data to anticipated and prevented premature hardware failures and reduce the resulting APU disruption. The fault data would be returned to Honeywell for analysis and presented to Hainan Airlines' maintenance team in a concise visual chart. Honeywell used this data to determine whether to repair an APU and to avoid unplanned out-of-date repair events. Tests have shown that Honeywell's preventive maintenance services reduced ineffective equipment by 35%, significantly reduced operational outages, and false positives below 1%. The industrial internet platform is promising to connect all resource units together for better operation. However, GE is going to divest itself of Predix, since the difficulty financial problem. Actually, the hard situation is essential because the current IIP cannot substantially improve the performance
2.2. The second stage of the industrial internet platform The exponential growth of industrial big data has forced an essential platform for analyzing the industrial big data. During the data analyzing, the current studies mainly focus on the efficiency of data modelling, extraction, integration, transformation, and preprocessing [27,28]. Actually, data is always important in the integration of enterprise systems to enable refined management and optimization [29]. Liu discussed the data integration by equivalence mapping between databases during the syntactic integration enterprise applications [30]. However, with the increasing volume of industrial big data, the traditional data integration framework cannot afford the requirement of data request, search and analysis [31,32]. To ingest large-scale heterogeneous industrial big data with privacy concerns, Wang et al. presented an industrial big data integration and sharing system with fog computing to transmit the intermediate data analysis result enabling distributed data analysis and integration [33]. Referring to the data extraction, Bala M, et al. proposed a fine-grained distribution approach 2
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obtain the switching state of different machines by the same command with different drivers. From the functional view, the industrial driver contains “3C-3I” structures: communication, computation, control, identification, insight and interoperation. Different from the driver in operation systems, the driver of a resource unit has functions of computing, and insight, which enable the industrial drivers to monitor, analysis, and forecast the status of resource units to achieve initiative, high efficient optimization with low time latency.
of manufacturing systems. From the perspective of system evolution, industrial Internet construction is still in its infancy. Some important gaps which remain to be filled are listed as follows: 1) Nowadays, manufacturing systems are usually reconstructed or reoptimized to meet the fast-changing and developing customers' need [49]. The resources units which refer to the individual resource in the system, such as robotics, sensors, and numerically-controlled machine tools can be reconfigured and restructured. So, there is a rising critical issue: how the IIPs adapt to the changing system structure? Aiming to realize the reconfiguration and restructure of the manufacturing system, all machines, robots, and automated guided vehicles (AGVs) should be “plug and play”, which means the function of the resource units and manufacturing execution mode can be restructured easily. 2) In manufacturing systems, it is important to note that the collaboration of the resource units will lead to the continuous evolution to a more cooperative information/knowledge-driven EIS environment. However, the current IIPs just simply connect devices together adopting a comparatively looser structure can hardly support the collaborative optimization for manufacturing systems. Therefore, in the Ind-OS, all the resource units should be connected together to organize a relation chain. With the relation chain, all resource units can be brought together to enable knowledge building, process monitoring, decision support, requirements management, and control. 3) With the IIP, the EIS companies can develop and release industrial Apps for users. And, different industrial Apps are coupled together to provide the operation of manufacturing systems [50]. The current IIP, however, appears to be too limited if we consider that two modules in different EISs interoperate with each other. Hence, the micro-service model is used and the interoperation method of microservices is designed in the Ind-OS, to realize the deep collaboration and integration of the industrial Apps.
3.1.1. Communication The communication in the industrial driver consists of two types of functions. First, the industrial driver can capture the status data from the resource units by multiple protocol transformation interfaces. Second, the industrial driver can communicate with the industrial platform through industrial internet, which can transmit status data to the IIP for process data collection and system operation. 3.1.2. Computation The computing refers to the data collected from the resource units should be analyzed in the industrial driver for specific purposes, such as machine fault detection, and basic data fusion. Actually, the industrial driver is only to finish some task with low time-complexity and timesensitive. For example, in the high-speed spinning process, due to the unevenness of the cotton material quality and the elastic force of the yarn during the spinning process, it is necessary to adaptively calculate and adjust the spinning tension, and the timeliness requirement can reach the millisecond level. Additionally, the complicated tasks with huge computational cost should be uploaded to the industrial platform for processing. 3.1.3. Control Another important function of an industrial driver is to control the resource or control the communication between the resource units and industrial internet platform, which can send instructions to the resource units for finishing the production tasks. For example, the driver of a CNC machine tool can actively control the machine tool by sending the NC program to adjust its running status, processing route or parameters for finishing the processing task.
3. The proposed architecture of industrial operation system The proposed industrial operation system is designed in this section (shown in Fig. 1), which consists of the edge layer, the PaaS layer, and the application layer. In the edge layer, an industrial driver is equipped for each resource unit. With the industrial driver, the resource units from different manufacturing systems can be attached in an Ind-OS. In the PaaS layer, the resource units can be organized according to production processes with the digital thread. All meta-data about the resource unit can be updated to the digital thread for the data warehouse construction in data analysis. As a result, manufacturing systems in a supply chain can collaborate with each other easily, since all resource units and meta-data can be accessed through the PaaS layer and edge layer. In the application layer, the EISs are decoupled into different micro-services for better collaboration. Enterprises can download suitable Apps to construct the “Jigsaw Apps” for the operation of manufacturing systems. For the cooperation of different enterprises, the interoperable mode between the different Apps is designed to finish the collaborative business flow in a supply chain.
3.1.4. Identification The industrial drivers should identify the resource units in manufacturing. With the driver in manufacturing systems, the IIP can be aware of all "on-line" resource units with all properties and status factors. 3.1.5. Insight The insight means the working status of the resource units are realtime monitored by the driver, which can indicate that whether the resource units works normally or not, and even more details information (operating state, running parameters, and task progress, etc.). 3.1.6. Interoperation During the system operation, the resource units cooperate together to finish the production tasks through the “3C” functions of industrial drivers. For example, the driver of a CNC machine tool would call the driver of a robot for uploading the job to finish the processing.
3.1. Industry driver for “plug and play” edge layer Aiming to realize “plug and play”, the resource units should be designed as standardized, configurable and intelligent. To achieve this goal, each resource unit should be equipped with an industry driver for better accessibility, which is an agent contains the corresponding hardware and software acting as an abstraction layer between the resource units and IIP. Every version of a resource unit, such as a customized hoist requires its own specific configuration. In contrast, most industrial applications can utilize resource units by means of high-level generic commands provided by different drivers. For example, we can
3.2. Digital thread enabled industrial PaaS layer The PaaS layer of the IIP provides the software operating environment and management of the resource units, which is an interlayer between the edge layer and application layer. Besides all the necessary components in a commercial PaaS platform, the PaaS layer of the IIP is equipped with the digital thread, which is the backbone of the IIP connecting all resource units in manufacturing systems. The digital thread consists of two parts: the resource chain and information flow. In 3
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Fig. 1. The framework of industrial operation system.
various criteria. Usually, they are organized in a tree structure, with resource units enumerating their children. When a resource unit is detected and identified by the identifier, the organizer finds the resource unit's industrial driver. If the industrial driver were loaded already, the organizer calls the driver to initialize that resource unit.
the vertical direction, the digital resource chain registers and identifies all resource units through the industrial drivers, and visualizes the organization of the resource units through the industrial drivers. In the horizontal direction, the integrated information flow relates all the metadata collected by the digital resource chain with each other in the industrial Apps of the product lifecycle about the information flow. So that all metadata in the ERP Apps, MES Apps and other Apps are mutual recognized and standardized. The unification and standardization of the metadata provide a solid foundation for the interoperability of the industrial Apps.
3.2.2. Integrated information chain The integrated information chain, which describes the information flow structure of the enterprise's working mechanism and technological process, connects the isolated resource units collected by digital resource chain into full lifecycle. The integrated information chain is a meta-data graph structure containing three functional units: nodes, edges and global information. The node is a placeholder populated by the code of source units defined in digital resource chain, which does not contain meta data itself but only unique encodings linked to the data. Each node in the integrated information chain represents an entity of a resource unit in the physical workshop. The edges is a vector describe the relationship between two nodes according to the data flow between resource units, which is a one-to-many relationship connecting any related nodes by unique key. A collection of these nodes and edges depicts the global information of a physical workshop. In this graph structure of information flow description, nodes, edges and global information are able to be adjusted and updated when the technological process changed or resource units rescheduled, which make the whole structures of the digital thread neatly reconfigurable and learnable.
3.2.1. Digital resource chain The digital resource chain is made up of an identifier and an organizer, which recognizes each resource unit and manages the relationship between the resource units. The identifier recognizes each resource unit with a namespace address (called uniform resource identifier, URI), which is generated based on a hierarchical naming rule as follows. The first-level subdirectory of the URI is determined by the resource attribute. Then, the second-level subdirectory corresponds to the belonging division of manufacturing systems. Next, the third-level subdirectory is defined according to the properties of the resource unit. For example, a welding gun named “O” can be addressed with the URI: “Tool/WS1/Weldinggun20190404” indicating that this resource unit is a type of machine tool belonging to the first workshop with the description ``Weldinggun20190404". In manufacturing systems, the IIP authentication service checks the uniqueness of the URI of every resource unit automatically and navigates to the online-resource units on the IIP through multiple layers of namespaces, firewalls, and sub-authentication servers. The organizer manages and links the abovementioned resource units, which are isolated entities with different URIs but actually interrelated in the production process. It allows users to view and control the resource unit attached to the IIP. When a piece of resource unit is abnormal, this resource unit will be highlighted for the engineers to deal with. The list of resource units can be sorted by
3.3. Interoperable application layer Aiming to achieve deeply interoperability of different modules of the EISs to derive the operation of enterprises shaping more agile comprehensive models, we proposed an application organization pattern which is named the application lake. In the application lake, a monolithic application is disassembled into several micro-services only focused on a specific function in a business logic with a well-designed 4
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client does not aggregate data but calls different micro-services based on the difference in business needs. For example, in a manufacturing system, we need to simultaneously monitor the real-time status of equipment and products. But we have an original dataset containing records in three dimensions: machine, time, and field. Each cell (m, f, t) of the data cube contains the value of the ``f" field of the ``m" machine at the time ``t". Hence, we need to parallel invoke the micro-service components that provide separate thematic data warehouses of equipment and products along the timeline and display the real-time status of equipment and products. 3.3.3. Chained mode Chained mode consists of a series of serial micro-service components that will generate a merged response upon receipt of the request. In this case, “Service A” will communicate with “Service B” upon receiving the request. Similarly, “Service B” will communicate with “Service C”. All services use synchronous messaging. The client will block until the entire chained call is completed. For example, in the maintenance decision of manufacturing system, the abnormal behaviors of the equipment or manufacturing process are detected by related sensors. The signal points would be regarded as anomalous when exceeding the upper control limit and the lower control limit through the signal monitoring service. Then, the corresponding data of process plan, equipment operating parameters, workers and others will be extracted through the data extraction service. Next, the root cause of the unusual deviation is diagnosed conjoint analysis of deviation data and assembling process through the special analysis service. As a result of such continuous service modules, recommendations for maintenance decision could be obtained.
Fig. 2. The interoperate modes of micro-service Apps.
external interface, each with its own archive, deployed separately, and then together form different kinds of applications. Thus, the industrial Apps in the application lake can interoperate with each other to improve the fast response ability. In this section, four interoperate modes are designed to cope with complicated business of EISs (shown in Fig. 2), which are single mode, aggregator mode, chained mode and shared mode.
3.3.4. Shared mode Shared mode is an extension of the aggregator mode and chained mode that allows multiple parallel micro-service chains to be called at the same time. In this mode, multiple service components call each other and work together by sharing caching and database stores to accomplish a common task. “Service A” and “Service C” are simultaneously invoked by a task, meanwhile, “Service A” will communicate with “Service B” and “Service C” upon receiving the request, “Service C” and “Service D” share an identical database. This would only make sense if there is a strong coupling between the two services. Some might consider this an anti-pattern but business needs might require in some cases to follow this. For example, during the product scheduling, it is necessary to consider the inventory information, logistics information, equipment situation, processing time, etc. However, the equipment situation and the processing time are closely related, they need to share the process database and work together to control the cycle time.
3.3.1. Single mode Single mode refers to packaging the functions of a single business application into several single units, which consists of a single service, cache, industrial driver or other external databases, a service with single mode can both acquire data from an industrial driver and other external databases. Cache is an essential key component for proposed architecture of high-concurrency and high-performance, which temporarily stores the data uploaded from variously distributed industrial drivers and databases in their respectively local memory to provide real-time data access services for a large number of requests in the workshop. However, different from the traditional monolithic applications, it lets each service manage its own database and provides a more lightweight communication mechanism. The proposed single mode advocate developers to interact with different process services by the mechanism that is not related to the development language and development platform, which are able to express complex information and transfer cross-platform information. For example, the data extraction service, which is a single mode, can flexibility to provide data extraction services for production process monitoring or manufacturing system analysis, rather than having to design a unique data extraction function for each different industry application. Developers only need to clarify the internal responsibilities of the functional components and the external interaction interfaces to build applications with good reusability and maintainability.
4. Case study To demonstrate the effectiveness of the proposed Ind-OS, an explanatory case study is conducted with the current EISs of a compressor assembly line. As shown in Fig. 3, the assembly of compressor consists of 24 steps of processes, which includes components selection, pump body assembly, welding, quality testing, etc. As the key step of compressor assembly, the pump body assembly is sensitive to the dimensions of cylinders, pistons, blades, etc. However, each component has its own dimensions, it is important to choose the matched components from the front machining and measuring processes. And the tightening force required by different size assembly combinations is also different, which needs to be quickly computed according to the matched components and carried out by robots in pump body assembly station. Therefore, the pump body assembly needs to invoke the data of the multiple previous processes and required the cooperation of multiple information systems, which is achieved through 3 steps: industry driver collocation, digital thread building and collaborative application development.
3.3.2. Aggregator mode Aggregator mode uses the aggregator to invoke multiple services simultaneously to implement the functionality required by the application. It can be a simple web page that processes the retrieved data. It can also be a higher-level combined micro-service that adds business logic to the retrieved data and then publishes it as a new micro-service. In addition, each service has its own cache and database, and the aggregator can be extended independently along the X and Z axes. The 5
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Fig. 3. The compressor assembling process.
Fig. 4. Workshop layout and industry driver collocation.
assembly lines and intermediate storages. The material is stored on the first floor of the factory, and transported to the station on the second floor by AGVs automatically according to the material requirement. According to the resource units distributed in these areas, 259 industry
4.1. Industry driver collocation As illustrated in Fig. 4, the two-story workshop is composed of four areas, which are components selection area, pump body lines, final 6
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Fig. 5. Compressor assembly digital resource chain.
codes and electronic tags attached to compressor parts will be automatically identified by the industry driver installed in the work position. Thus, the industrial operation system can be aware of the properties, position and status of the compressor part to determine if it is qualified. If yes, the robot will grab the part and move it to the workbench. Due to the different assembly parameters such as the coaxiality, upper eccentricity and lower eccentricity of each pump body, the industry driver will quickly compute the processing parameters such as machining position and torque for each pump according to its own assembly parameters. As well as the process parameters of each pump
drivers collocated in the workshop can be divided into 9 categories: AGV, automation equipment, product, laser engraved code, quality test, board, workshop environment, material storage, suspended chain. In the compressor pump body automatic assembly line, first, the industrial driver can automatically sense the current status of the AGV by wireless sensor networks. Then, the status data can be transmitted to the AGV scheduling App through industry network. Next, the nearest free AGV will be called to move the required material or tool to the work station through the communication of the industrial platform. When the material to be processed arriving the work position, the QR 7
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Fig. 6. Compressor assembly integrated information chain.
Fig. 7. Interoperable application lake.
4.2. Digital thread throughout the product lifecycle
body are computed, the corresponding instruction such as NC program or PLC program will be generated and sent to the machining tool to control its running status, processing routes or parameters for performing the processing task. During the processing stage, the current status of the storage, logistics and machine tools (operating state, running parameters, and task progress, etc.) are real-time monitored by the industry driver, which provide deep insight to visualize the manufacturing state in the Ind-OS. When the current batch of the compressor pump body is monitored to be nearly finished, the industry driver of a machining robot will interoperate across the services to call the driver of an AGV to transport the next batch of material to be processed to the working position.
After deploying the industry driver in compressor manufacturing workshop, the manufacturing process data captured from all processes is interconnected to build the whole digital thread throughout the compressor product lifecycle, which is the blood vessel of the Ind-OS. As shown in Fig. 5, the digital resource chain is an integration of all resource units in the compressor assembly line, which is easy to be managed and reconfigured when the resource units changed with the business change. It is composed of material storage, AGV, automation equipment, product/component, laser spray code machines, quality test, workshop environment and workers. In each branch, the corresponding industry driver, attributes data, and real-time data are chained with the essential resource units in the compressor assembly processing. Meanwhile, as shown in Fig. 6, the compressor assembly 8
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HCM, ERP and SRM are developed through the shared mode. Most of them are joined from some common micro-service components, which can also provide independent service. Therefore, in the real operation environment, the applications can interoperate together to collaboratively perform the compressor production task. Due to its ``Jigsaw" characteristic, users can quickly recombine a new application from a variety of micro-service components in application lake when the business changes or research requirements, such as the LIMS, Trial-MES and Think Tank. In the compressor pump body automatic assembly process, which is achieved by the cooperation of robots, machining tool, AGV, MES, PCS and PLC, is carried out in Fig. 8. At the beginning of the compressor pump body assembly process, the MES calls the RCS to transport the required components to the work station by AGVs. When the material to be processed arriving the work position, the RCS tells the MES to change the state of the process. Then, the PCS calls the PLC to automatically scan the QR codes attached to the components and upload the information to MES. According to this data, the assembly parameters such as machining position and torque for each pump body will be computed by MAS. Next, instruction is generated through the PCS to command the PLC of robots to carry out the assembly operation. After the current batch of compressor pump body finished, the MES will automatically call the AGV to transport the tray to the designated area. When the AGV arrives in front of the equipment grating door, the information interaction between the RCS and MES requests the grating door to be closed and opened, ensuring efficient, fast, safe and orderly production on site.
Table 1 The abbreviations of application system appeared in this case. Serial number
Abbreviations
Description
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
AGV PLC Device RCS PCS WMS MES QMS LMS LIMS EMS EHS PLM HCM ERP SRM HR CRM MAS FAS KDA
Automated Guided Vehicle Programmable Logic Controller Production Equipment Operation System Robot Control System Process Control System Warehouse Management System Manufacturing Execution System Quality Management System Labor Management System Laboratory Management System Energy Management System Environment Health System Product Lifecycle Management Human Capital Management Enterprise Resource Planning Supplier Relationship Management Human Resource Customer Relationship Management Manufacture Analysis System Finance Analysis System Knowledge Data Analysis
integrated information chain is organized throughout the compressor assembly lifecycle. In each work step, the resource units that need to be used are employed and linked to the corresponding process. For example, in order to achieve above “3C-3I” function of industry driver to collaboratively accomplish the task of pump assembly process, the data of material storage is provided to the AGV to transfer the nearest material. Further, the data of components are provided to the industry driver to compute the optimized the assembly parameters and command the assembly robot to complete the corresponding operation. All of the above operations are based on the digital thread interconnected the dispersed metadata.
5. Discussion The proposed Ind-OS is compared with the currently famous IIPs of GE Predix [40], ABB Ability [41], Siemens MindSphere [5] and PTC ThingWorx [6] from the view of connection, computing, integration and APPs development. As is suggested by Table 2, the proposed Ind-OS can provided more flexible connection mode for resource units, fogcloud computing mode and reconfigurable industrial applications.
4.3. Interoperable application lake
5.1. The impact on computing mode
The third step is the collaborative application development, which is the carrier of all assembly business. According to the business requirements of this compressor manufacturing company, the related application software divided into six layers are developed through the proposed four design modes. As shown in Fig. 7 and Table 1, the LMS, HR and CRM are developed through the single mode. The MAS, FAS and KDA are developed through the aggregator mode. The AGV, RCS, WMS, and PLM are developed through the chained mode. The PLM,
With the current EISs architecture, all the data are transmitted to the cloud platform and analyzed on the EIS servers. In manufacturing systems, some computing tasks are time sensitive. For example, the photograph of fabric should be analyzed within 100 ms to figure out the control signal of a knitting machine. In such scenarios, the cloud-based computing model can hardly meet the requirements of time latency. In
Fig. 8. Collaborative operation during compressor pump body automatic assembly. 9
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Customized
Customized
Open partner ecosystems
NavigateView (Internal and external users can actively participate in the development of the key functions) Jigsaw APPs
Departments of inner-enterprise
Product life cycle
Product life cycle & Internal enterprises chain
Product life cycle
Cloud computing
Fog-cloud computing
Cloud-based Internet of things computing Fog-cloud computing
5.2. The impact on industrial ecosystem In the current manufacturing systems, different EISs are isolated or weakly connected with each other, since the lack of standardization of resource units and mutual recognition of meta-data. With a larger view, the enterprises on the upstream and downstream of an industrial ecosystem can hardly be connected together for the more effective supply chain. In the proposed Ind-OS, the digital thread standardizes and links all the resource units together from various disciplines through the digital resource chain and the integrated information flow with the goal of enabling tasks across traditionally isolated disciplines. For example, the data of material, machine status, work-in-process, processing parameters can be correlated analyzed to improve the product quality. Further, by connecting all digital threads of the enterprises on the supply chain together, the data, information and knowledge can be accessed end-to-end on the Ind-OS, which finally results in a more agile and effective industrial ecosystem. 5.3. The impact on enterprise informationization
Fog-cloud computing
During the implementation of EISs, there are a lot of customized developing according to the individualized requirements of different enterprises, which leads to high industrial software cost. In the proposed Ind-OS, the huge EISs are decoupled into massive micro-services, and the users can select suitable micro-services to form "Jigsaw APPs" to meet the industrial requirements. Correspondingly, developers can release and upgrade the micro-services applications on the platforms. With the micro-service architecture in Ind-OS, the EISs are closer to the small-business, since the industrial software cost can be reduced by the decreasing of customized developing. Furthermore, the users can recombine the "Jigsaw APPs" quickly to cope with the new requirements of manufacturing systems, which is critical for quick transformation and upgrading of enterprises with an uncertain market. 6. Conclusion In this paper, an IIP framework called Ind-OS is proposed, which is made up of an edge layer with industry drivers, an industrial PaaS layer with digital thread, and an interoperable application layer. The proposed Ind-OS is a systematic framework focusing on the operation of manufacturing systems, which is likely to become the next generation of IIP for manufacturing systems. Compared with the current cloud-based EISs architecture, the IndOS can provide collaborative services from computing mode, industrial ecosystem, and industrial App. 1) In the edge layer of the Ind-OS, the industrial driver has the function of “3C-3I” structures: communication, computation, control, identification, insight and interoperation, which is adopted to realize the "plug and play" of resource units to the industrial internet platform. With the computation, insight, and interoperation services, the complex computing tasks can be finished with a collaborative scheme. The large tasks with huge computational cost can be
Ind-OS
PTC ThingWorx
Siemens MindSphere
ABB Ability
Fixed deployment (Wide connection protocols) Fixed deployment (Wide connection protocols) Fixed deployment (MindSphere connection protocol) Fixed deployment (End-to-end integration IIOT) Plug-and-play (Industrial driver) GE Predix
the Ind-OS, the computing is further extended to the edge of the platform. Lightweight time-sensitive tasks can be finished in the fog-edges by industrial drivers, and complex tasks with huge computational cost can be conducted on the platform. Moreover, with the designed digital thread connecting all resource units and meta-data, the global production information even in different APPs can be integrated to realize collaborative computing of multi-tasks. As a result, faster responses, better optimization and control can be achieved with the industrial driver. Compared with the cloud-based computing model in the current EISs architecture, the Ind-OS can provide multi-scaled computing service in the fog-cloud architecture, to empower the complex computing tasks with the collaborative scheme.
Product life cycle & Enterprises across industrial chain
APPs development mode Integrated mode Computing mode Connection mode IIPs
Table 2 Property comparison about the IIPs.
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conducted on the powerful cloud computing platform, and the urgent task with small computational cost and low latency can be finished by industrial drivers directly. 2) Different from the current IIPs, the PaaS layer in the Ind-OS is equipped with the digital thread, which consists of a digital resource chain and an integrated information chain. During the construction of the digital thread, the resource units are standardized and the meta-data in the EISs are authenticated. The industrial systems can be easily reconstructed and reconfigured for the uncertain issues. As a result, the physical resource units and cyber software applications are tightly collaborated together with to achieve higher production efficiency. 3) In the application layer, the industrial applications are decoupled into different micro-services in the application lake. With the industrial driver and digital thread, the function of each EIS can be realized through the interoperation of different micro-services with four modes: single mode, aggregator mode, chained mode and shared mode. With the proposed application lake, the suitable micro-services can be collaborated to form a “Jigsaw APPs” to meet the industrial requirements, and the “Jigsaw APPs” can be updated quickly to cope with the new requirements of manufacturing systems.
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At present, the IIP is still in its infancy. In future, we will consider to construct a mini Ind-OS platform in different particular production systems, such as wafer manufacturing system, welding production system. The second limitation lies in the implementation of the proposed Ind-OS, which will be programmed and embedded in the enterprise systems. In the future, manufacturing systems with a comprehensive domain model will be conducted. Declaration of Competing Interest The authors promise that there is no conflict of interest about this paper. Acknowledgements This work was supported by the Program of the National Natural Science Foundation of China under Grant nos. 51905091 and 51435009, and Sailing Program of Shanghai Science and Technology Committee under Grant no. 19YF1401500. References [1] N. Jazdi, Cyber physical systems in the context of industry 4.0, IEEE International Conference on Automation, 2014. [2] J. Lee, B. Bagheri, H.A. Kao, A cyber-physical systems architecture for industry 4.0based manufacturing systems, Manuf. Lett. 3 (2015) 18–23. [3] H. Panetto, et al., New perspectives for the future interoperable enterprise systems, Comput. Ind. 79 (C) (2016) 47–63. [4] Zhang, Y., et al., Long/short-term utility aware optimal selection of manufacturing service composition towards industrial internet platform. IEEE Transactions on. Industrial Industr. Informatics. [5] U. Lichtenthaler, Substitute or synthesis: the interplay between human and artificial intelligence, Res.-Technol. Manag. 61 (5) (2018) 12–14. [6] T. Fernándezcaramés, et al., A fog computing based cyber-physical system for the automation of pipe-related tasks in the industry 4.0 shipyard, Sensors 18 (6) (2018) 1961. [7] M. Wollschlaeger, T. Sauter, J. Jasperneite, The future of industrial communication: automation networks in the era of the internet of things and industry 4.0, IEEE Industr. Electron. Mag. 11 (1) (2017) 17–27. [8] W. Shao, et al., A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances for hypoid gear product, Robot. Comput. Integr. Manuf. 54 (2018) 1–16. [9] K. Menon, H. Kärkkäinen, J.P. Gupta, Role of industrial internet platforms in the management of product lifecycle related information and knowledge, Ifip International Conference on Product Lifecycle Management, 2016. [10] S. Al-Rubaye, et al., Industrial internet of things driven by SDN platform for smart grid resiliency, IEEE Int. Things J. (2017) PP(99): p. 1-1. [11] I. Seruca, et al., Proceedings of the 6th international conference on enterprise information systems (ICEIS 2004), Microcirculat. Endothelium Lymphatics 4 (4)
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Full length Article
A collaborative architecture of the industrial internet platform for manufacturing systems
T
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Wang Junlianga, Xu Chuqiaob, Zhang Jiea, , Bao Jingsonga, Zhong Rayc a
College of Mechanical Engineering, Donghua University, Shanghai, China School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China c Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong b
A R T I C LE I N FO
A B S T R A C T
Keywords: Industrial internet Fog computing Digital thread Micro-service
One of the most significant advances in the development of intelligent manufacturing is represented by the industrial Internet, which is combining the physical and cyber components in manufacturing systems together. Aiming to manage the interaction between the physical and cyber components, this paper proposes a collaborative architecture for industrial Internet platform (IIP) called industrial operation system (Ind-OS), which contains the industrial driver, digital thread and micro-services to provide a better cooperative enterprise information system (EIS) environment for manufacturing systems. The industrial driver in the edge layer is presented for each resource unit for communication, computation, control, identification, insight and interoperation, realizing a ``plug and play" fashion in IIP. The digital thread is designed to link all resource units and metadata, which consists of a digital resource chain and an integrated information chain. In the application layer, all the businesses in EIS are decoupled into different micro-services, and a “Jigsaw Apps” is recombined to support the operation of manufacturing systems. A case study illustrates the effectiveness of the proposed Ind-OS, and the impacts caused by the Ind-OS are discussed.
1. Introduction In the Industry 4.0 era, it has become a trend that fusing the physical and cyber components together gain insight into the industrial process from the data for improving productivity, efficiency and reliability [1,2]. The industrial Internet platforms (IIPs) manage the interaction between the physical and cyber components, which are the core in operating the industrial systems. Extensive works have been done with IIP in the past several decades [3,4], such as GE Predix, ABB Ability, Siemens MindSphere, PTC ThingWorx, etc. The current platforms extend the managing domain to cover all stages of a product life cycle. For example, Siemens developed an IIP called MindSphere to realize the interconnection of about 1 million devices and systems and provide predictive maintaining services for the devices [5]. The IIPs link the physical and cyber components together, capacitating the resource, data, and knowledge available everywhere in manufacturing systems, have emerged as a rising hotpot in the academic and industrial circle [6–8]. The current IIPs mainly concerned about the maintenance of intelligent products, and the research on operation of manufacturing systems is limited [9,10]. Nowadays, the operation of manufacturing
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systems are realized through various enterprise information systems (EIS) [11], including human resource management (HRM) systems [12], customer relationship management (CRM) systems [13], enterprise resource planning (ERP) systems [14], manufacturing executive system (MES) [15,16], etc. The collaboration between these EISs is difficult since they are usually developed by different companies in various periods, resulting in several information islands. While it is also important to note that an EIS should be collaborative with other systems to meet the rapidly changing market of an enterprise. For example, product designers will consider the customer's preference obtained from the CRM system [17]. The customer's data should link to the design data to support collaborative optimization. With the requirements of the initiative collaboration scheme, different EISs should be operated and connected together for an effective management framework of the enterprise systems. This is significant when the enterprises in the supply chain are integrating together to provide services for the customers. And this, in turn, will lead to the continuous evolution to a more cooperative information/knowledge-driven EIS environment [18]. The IIP integrates physical and cyber components together weakening the boundary of different EISs to build a reconfigurable and
Corresponding author. E-mail address: [email protected] (J. Zhang).
https://doi.org/10.1016/j.rcim.2019.101854 Received 25 April 2019; Received in revised form 8 August 2019; Accepted 16 August 2019 0736-5845/ © 2019 Published by Elsevier Ltd.
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to extract data from different datasets of EISs [34]. Sun et al. proposed a new data integration method. After converting reward-complement balanced data set into multiple balanced data sets, a number of classifiers are constructed by using specific classification algorithms, and the classification results are combined by specific integration rules [35]. Wen et al. proposed a new design and development of ETL system for marine environmental data cleaning, transformation and integration, and offered data access interfaces [36]. Chen, et al. improved the algorithm applied to large data integration learning, proposed a new parallel random forest (PRF) algorithm on the Apache Spark platform [37]. Di et al. proposes a new innovative design method, GrHyMM, which can integrate structured and unstructured heterogeneous raw data [38]. Andrzejak et al. proposed an efficient merging method of decision trees into a single decision tree, for parallel learning and learning from distributed data [39]. Peter Baumann et al. presented and discussed the main results of the Earth Server project and expound the analysis and application of earth big data [40]. Soltanpoor et al. proposed a descriptive, predictive and prescriptive big data analytics framework, which links the extracted insight from the data to the pertinent generated actions [41]. Wen et al. proposed a method to analyze data sets by using neural network modelling, which is related to data mining and fault diagnosis [42]. At the second stage of the industrial platform, the big data platform is now emerging to be one of the core technologies that underlie different kinds of EISs to achieve a better collaborative optimization ability through big data analytics [43,44]. However, in the Industry 4.0 era, manufacturing systems are facing new challenges in the ever-changing dynamic environment [45]. As a result, the big data platform only integrating different EISs together can hardly acclimatize to the fast-paced changing manufacturing systems.
ubiquitous service environment [19], which provides a good condition for end-to-end collaboration. The loose coupling structure is beneficial for system resilience [20,21], since the service and system structure can be adjusted according to dynamic events. This paper proposed an industrial operation system (Ind-OS) for manufacturing systems with the architecture of IIP to enable easier collaboration of EISs. The rest of this manuscript is organized as follows: Section 2 reviews the brief history of the industrial internet platform. Section 3 designs the referenced framework of the Ind-OS, including industrial driver for edge layer, digital thread for industrial PaaS layer, and the interoperable application layer. A case study is conducted in Section 4 to illustrate the effectiveness and feasibility of the proposed Ind-OS. The difference between the proposed Ind-OS and the current EIS architecture is discussed in Section 5. Finally, conclusions and recommendations for future work are summarized in Section 6. 2. Brief history 2.1. The first stage of the industrial internet platform The first generation of the IIP is essentially an industrial platform in SaaS (Software as a Service) model with several enterprise information systems (e.g. Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Supply Chain Management (SCM)) to support the operation of manufacturing systems. These EISs are developed toward cloud computing architecture and implemented on a PaaS platform (e.g. Windows Azure, Google App Engine, and Force.com) to provide operation service for enterprises [22]. Oppong et al. developed a workflow management system (WfMS) to improve the process efficiency by information availability improvement, process standardization, task assignment on an automatic basis to track process-related information and the status of each instance process [23]. Li et al. developed a customer relationship management (CRM) system oriented SaaS, which manages existing customers and explores further business opportunities with a cross-industry standard process for data mining (CRISP-DM) methodology [24]. Marcello et al. proposed a business process management (BPM) system on a SaaS platform to reduce the responding time of business tasks with changing environment [25]. To manage the different EISs on the SaaS platform, Li et al. presented a hybrid wireless network integration scheme to filter the suitable and available cloud services with service access requirements and user security credentials [26]. Usually, these EISs are developed by different suppliers with diverse software architectures in various periods. As a result, EISs in manufacturing systems are isolated with each other, which are well known as “isolated islands”. With the requirements of industrial data sharing and analysis, the SaaS model can hardly meet the fast development of EISs.
2.3. The third stage of the industrial internet platform The increasing connectivity provided by the Internet of Things (IoT) supports novel opportunities for the IIP to meld the physical industrial components and EISs into an integral whole. Many companies have proposed industrial internet platforms to seize the commanding heights of the new industrial revolution. The Industrial Internet Consortium (IIC) is the world's leading organization transforming business and society by accelerating the adoption of the Industrial Internet of Things (IIoT), and oneM2M, the global IoT standards initiative. As the leading company of the IIC, GE released Predix, an industrial Internet platform released by IIC's lead enterprise GE, has achieved access to more than 10 areas of equipment [46]. ABB set up a new digital teleservice center for energy-efficient inverters in Bangalore, which provided end-to-end remote access to frequency converters in end-user plants for predictive maintenance and condition monitoring [47]. The next-generation digital solutions and services of ABB would be developed and built on the Microsoft Azure cloud platform and worked with IBM Watson IoT cognitive computing to create real-time cognitive analysis capabilities in smart factory. Honeywell developed a predictive maintenance service named GoDirect for connected auxiliary power units (APUs), and Hainan Airlines became the first airline in the world to adopt GoDirect [48]. The interconnected APU service used the data connections already on the aircraft to download APU maintenance and fault data to anticipated and prevented premature hardware failures and reduce the resulting APU disruption. The fault data would be returned to Honeywell for analysis and presented to Hainan Airlines' maintenance team in a concise visual chart. Honeywell used this data to determine whether to repair an APU and to avoid unplanned out-of-date repair events. Tests have shown that Honeywell's preventive maintenance services reduced ineffective equipment by 35%, significantly reduced operational outages, and false positives below 1%. The industrial internet platform is promising to connect all resource units together for better operation. However, GE is going to divest itself of Predix, since the difficulty financial problem. Actually, the hard situation is essential because the current IIP cannot substantially improve the performance
2.2. The second stage of the industrial internet platform The exponential growth of industrial big data has forced an essential platform for analyzing the industrial big data. During the data analyzing, the current studies mainly focus on the efficiency of data modelling, extraction, integration, transformation, and preprocessing [27,28]. Actually, data is always important in the integration of enterprise systems to enable refined management and optimization [29]. Liu discussed the data integration by equivalence mapping between databases during the syntactic integration enterprise applications [30]. However, with the increasing volume of industrial big data, the traditional data integration framework cannot afford the requirement of data request, search and analysis [31,32]. To ingest large-scale heterogeneous industrial big data with privacy concerns, Wang et al. presented an industrial big data integration and sharing system with fog computing to transmit the intermediate data analysis result enabling distributed data analysis and integration [33]. Referring to the data extraction, Bala M, et al. proposed a fine-grained distribution approach 2
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obtain the switching state of different machines by the same command with different drivers. From the functional view, the industrial driver contains “3C-3I” structures: communication, computation, control, identification, insight and interoperation. Different from the driver in operation systems, the driver of a resource unit has functions of computing, and insight, which enable the industrial drivers to monitor, analysis, and forecast the status of resource units to achieve initiative, high efficient optimization with low time latency.
of manufacturing systems. From the perspective of system evolution, industrial Internet construction is still in its infancy. Some important gaps which remain to be filled are listed as follows: 1) Nowadays, manufacturing systems are usually reconstructed or reoptimized to meet the fast-changing and developing customers' need [49]. The resources units which refer to the individual resource in the system, such as robotics, sensors, and numerically-controlled machine tools can be reconfigured and restructured. So, there is a rising critical issue: how the IIPs adapt to the changing system structure? Aiming to realize the reconfiguration and restructure of the manufacturing system, all machines, robots, and automated guided vehicles (AGVs) should be “plug and play”, which means the function of the resource units and manufacturing execution mode can be restructured easily. 2) In manufacturing systems, it is important to note that the collaboration of the resource units will lead to the continuous evolution to a more cooperative information/knowledge-driven EIS environment. However, the current IIPs just simply connect devices together adopting a comparatively looser structure can hardly support the collaborative optimization for manufacturing systems. Therefore, in the Ind-OS, all the resource units should be connected together to organize a relation chain. With the relation chain, all resource units can be brought together to enable knowledge building, process monitoring, decision support, requirements management, and control. 3) With the IIP, the EIS companies can develop and release industrial Apps for users. And, different industrial Apps are coupled together to provide the operation of manufacturing systems [50]. The current IIP, however, appears to be too limited if we consider that two modules in different EISs interoperate with each other. Hence, the micro-service model is used and the interoperation method of microservices is designed in the Ind-OS, to realize the deep collaboration and integration of the industrial Apps.
3.1.1. Communication The communication in the industrial driver consists of two types of functions. First, the industrial driver can capture the status data from the resource units by multiple protocol transformation interfaces. Second, the industrial driver can communicate with the industrial platform through industrial internet, which can transmit status data to the IIP for process data collection and system operation. 3.1.2. Computation The computing refers to the data collected from the resource units should be analyzed in the industrial driver for specific purposes, such as machine fault detection, and basic data fusion. Actually, the industrial driver is only to finish some task with low time-complexity and timesensitive. For example, in the high-speed spinning process, due to the unevenness of the cotton material quality and the elastic force of the yarn during the spinning process, it is necessary to adaptively calculate and adjust the spinning tension, and the timeliness requirement can reach the millisecond level. Additionally, the complicated tasks with huge computational cost should be uploaded to the industrial platform for processing. 3.1.3. Control Another important function of an industrial driver is to control the resource or control the communication between the resource units and industrial internet platform, which can send instructions to the resource units for finishing the production tasks. For example, the driver of a CNC machine tool can actively control the machine tool by sending the NC program to adjust its running status, processing route or parameters for finishing the processing task.
3. The proposed architecture of industrial operation system The proposed industrial operation system is designed in this section (shown in Fig. 1), which consists of the edge layer, the PaaS layer, and the application layer. In the edge layer, an industrial driver is equipped for each resource unit. With the industrial driver, the resource units from different manufacturing systems can be attached in an Ind-OS. In the PaaS layer, the resource units can be organized according to production processes with the digital thread. All meta-data about the resource unit can be updated to the digital thread for the data warehouse construction in data analysis. As a result, manufacturing systems in a supply chain can collaborate with each other easily, since all resource units and meta-data can be accessed through the PaaS layer and edge layer. In the application layer, the EISs are decoupled into different micro-services for better collaboration. Enterprises can download suitable Apps to construct the “Jigsaw Apps” for the operation of manufacturing systems. For the cooperation of different enterprises, the interoperable mode between the different Apps is designed to finish the collaborative business flow in a supply chain.
3.1.4. Identification The industrial drivers should identify the resource units in manufacturing. With the driver in manufacturing systems, the IIP can be aware of all "on-line" resource units with all properties and status factors. 3.1.5. Insight The insight means the working status of the resource units are realtime monitored by the driver, which can indicate that whether the resource units works normally or not, and even more details information (operating state, running parameters, and task progress, etc.). 3.1.6. Interoperation During the system operation, the resource units cooperate together to finish the production tasks through the “3C” functions of industrial drivers. For example, the driver of a CNC machine tool would call the driver of a robot for uploading the job to finish the processing.
3.1. Industry driver for “plug and play” edge layer Aiming to realize “plug and play”, the resource units should be designed as standardized, configurable and intelligent. To achieve this goal, each resource unit should be equipped with an industry driver for better accessibility, which is an agent contains the corresponding hardware and software acting as an abstraction layer between the resource units and IIP. Every version of a resource unit, such as a customized hoist requires its own specific configuration. In contrast, most industrial applications can utilize resource units by means of high-level generic commands provided by different drivers. For example, we can
3.2. Digital thread enabled industrial PaaS layer The PaaS layer of the IIP provides the software operating environment and management of the resource units, which is an interlayer between the edge layer and application layer. Besides all the necessary components in a commercial PaaS platform, the PaaS layer of the IIP is equipped with the digital thread, which is the backbone of the IIP connecting all resource units in manufacturing systems. The digital thread consists of two parts: the resource chain and information flow. In 3
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Fig. 1. The framework of industrial operation system.
various criteria. Usually, they are organized in a tree structure, with resource units enumerating their children. When a resource unit is detected and identified by the identifier, the organizer finds the resource unit's industrial driver. If the industrial driver were loaded already, the organizer calls the driver to initialize that resource unit.
the vertical direction, the digital resource chain registers and identifies all resource units through the industrial drivers, and visualizes the organization of the resource units through the industrial drivers. In the horizontal direction, the integrated information flow relates all the metadata collected by the digital resource chain with each other in the industrial Apps of the product lifecycle about the information flow. So that all metadata in the ERP Apps, MES Apps and other Apps are mutual recognized and standardized. The unification and standardization of the metadata provide a solid foundation for the interoperability of the industrial Apps.
3.2.2. Integrated information chain The integrated information chain, which describes the information flow structure of the enterprise's working mechanism and technological process, connects the isolated resource units collected by digital resource chain into full lifecycle. The integrated information chain is a meta-data graph structure containing three functional units: nodes, edges and global information. The node is a placeholder populated by the code of source units defined in digital resource chain, which does not contain meta data itself but only unique encodings linked to the data. Each node in the integrated information chain represents an entity of a resource unit in the physical workshop. The edges is a vector describe the relationship between two nodes according to the data flow between resource units, which is a one-to-many relationship connecting any related nodes by unique key. A collection of these nodes and edges depicts the global information of a physical workshop. In this graph structure of information flow description, nodes, edges and global information are able to be adjusted and updated when the technological process changed or resource units rescheduled, which make the whole structures of the digital thread neatly reconfigurable and learnable.
3.2.1. Digital resource chain The digital resource chain is made up of an identifier and an organizer, which recognizes each resource unit and manages the relationship between the resource units. The identifier recognizes each resource unit with a namespace address (called uniform resource identifier, URI), which is generated based on a hierarchical naming rule as follows. The first-level subdirectory of the URI is determined by the resource attribute. Then, the second-level subdirectory corresponds to the belonging division of manufacturing systems. Next, the third-level subdirectory is defined according to the properties of the resource unit. For example, a welding gun named “O” can be addressed with the URI: “Tool/WS1/Weldinggun20190404” indicating that this resource unit is a type of machine tool belonging to the first workshop with the description ``Weldinggun20190404". In manufacturing systems, the IIP authentication service checks the uniqueness of the URI of every resource unit automatically and navigates to the online-resource units on the IIP through multiple layers of namespaces, firewalls, and sub-authentication servers. The organizer manages and links the abovementioned resource units, which are isolated entities with different URIs but actually interrelated in the production process. It allows users to view and control the resource unit attached to the IIP. When a piece of resource unit is abnormal, this resource unit will be highlighted for the engineers to deal with. The list of resource units can be sorted by
3.3. Interoperable application layer Aiming to achieve deeply interoperability of different modules of the EISs to derive the operation of enterprises shaping more agile comprehensive models, we proposed an application organization pattern which is named the application lake. In the application lake, a monolithic application is disassembled into several micro-services only focused on a specific function in a business logic with a well-designed 4
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client does not aggregate data but calls different micro-services based on the difference in business needs. For example, in a manufacturing system, we need to simultaneously monitor the real-time status of equipment and products. But we have an original dataset containing records in three dimensions: machine, time, and field. Each cell (m, f, t) of the data cube contains the value of the ``f" field of the ``m" machine at the time ``t". Hence, we need to parallel invoke the micro-service components that provide separate thematic data warehouses of equipment and products along the timeline and display the real-time status of equipment and products. 3.3.3. Chained mode Chained mode consists of a series of serial micro-service components that will generate a merged response upon receipt of the request. In this case, “Service A” will communicate with “Service B” upon receiving the request. Similarly, “Service B” will communicate with “Service C”. All services use synchronous messaging. The client will block until the entire chained call is completed. For example, in the maintenance decision of manufacturing system, the abnormal behaviors of the equipment or manufacturing process are detected by related sensors. The signal points would be regarded as anomalous when exceeding the upper control limit and the lower control limit through the signal monitoring service. Then, the corresponding data of process plan, equipment operating parameters, workers and others will be extracted through the data extraction service. Next, the root cause of the unusual deviation is diagnosed conjoint analysis of deviation data and assembling process through the special analysis service. As a result of such continuous service modules, recommendations for maintenance decision could be obtained.
Fig. 2. The interoperate modes of micro-service Apps.
external interface, each with its own archive, deployed separately, and then together form different kinds of applications. Thus, the industrial Apps in the application lake can interoperate with each other to improve the fast response ability. In this section, four interoperate modes are designed to cope with complicated business of EISs (shown in Fig. 2), which are single mode, aggregator mode, chained mode and shared mode.
3.3.4. Shared mode Shared mode is an extension of the aggregator mode and chained mode that allows multiple parallel micro-service chains to be called at the same time. In this mode, multiple service components call each other and work together by sharing caching and database stores to accomplish a common task. “Service A” and “Service C” are simultaneously invoked by a task, meanwhile, “Service A” will communicate with “Service B” and “Service C” upon receiving the request, “Service C” and “Service D” share an identical database. This would only make sense if there is a strong coupling between the two services. Some might consider this an anti-pattern but business needs might require in some cases to follow this. For example, during the product scheduling, it is necessary to consider the inventory information, logistics information, equipment situation, processing time, etc. However, the equipment situation and the processing time are closely related, they need to share the process database and work together to control the cycle time.
3.3.1. Single mode Single mode refers to packaging the functions of a single business application into several single units, which consists of a single service, cache, industrial driver or other external databases, a service with single mode can both acquire data from an industrial driver and other external databases. Cache is an essential key component for proposed architecture of high-concurrency and high-performance, which temporarily stores the data uploaded from variously distributed industrial drivers and databases in their respectively local memory to provide real-time data access services for a large number of requests in the workshop. However, different from the traditional monolithic applications, it lets each service manage its own database and provides a more lightweight communication mechanism. The proposed single mode advocate developers to interact with different process services by the mechanism that is not related to the development language and development platform, which are able to express complex information and transfer cross-platform information. For example, the data extraction service, which is a single mode, can flexibility to provide data extraction services for production process monitoring or manufacturing system analysis, rather than having to design a unique data extraction function for each different industry application. Developers only need to clarify the internal responsibilities of the functional components and the external interaction interfaces to build applications with good reusability and maintainability.
4. Case study To demonstrate the effectiveness of the proposed Ind-OS, an explanatory case study is conducted with the current EISs of a compressor assembly line. As shown in Fig. 3, the assembly of compressor consists of 24 steps of processes, which includes components selection, pump body assembly, welding, quality testing, etc. As the key step of compressor assembly, the pump body assembly is sensitive to the dimensions of cylinders, pistons, blades, etc. However, each component has its own dimensions, it is important to choose the matched components from the front machining and measuring processes. And the tightening force required by different size assembly combinations is also different, which needs to be quickly computed according to the matched components and carried out by robots in pump body assembly station. Therefore, the pump body assembly needs to invoke the data of the multiple previous processes and required the cooperation of multiple information systems, which is achieved through 3 steps: industry driver collocation, digital thread building and collaborative application development.
3.3.2. Aggregator mode Aggregator mode uses the aggregator to invoke multiple services simultaneously to implement the functionality required by the application. It can be a simple web page that processes the retrieved data. It can also be a higher-level combined micro-service that adds business logic to the retrieved data and then publishes it as a new micro-service. In addition, each service has its own cache and database, and the aggregator can be extended independently along the X and Z axes. The 5
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Fig. 3. The compressor assembling process.
Fig. 4. Workshop layout and industry driver collocation.
assembly lines and intermediate storages. The material is stored on the first floor of the factory, and transported to the station on the second floor by AGVs automatically according to the material requirement. According to the resource units distributed in these areas, 259 industry
4.1. Industry driver collocation As illustrated in Fig. 4, the two-story workshop is composed of four areas, which are components selection area, pump body lines, final 6
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Fig. 5. Compressor assembly digital resource chain.
codes and electronic tags attached to compressor parts will be automatically identified by the industry driver installed in the work position. Thus, the industrial operation system can be aware of the properties, position and status of the compressor part to determine if it is qualified. If yes, the robot will grab the part and move it to the workbench. Due to the different assembly parameters such as the coaxiality, upper eccentricity and lower eccentricity of each pump body, the industry driver will quickly compute the processing parameters such as machining position and torque for each pump according to its own assembly parameters. As well as the process parameters of each pump
drivers collocated in the workshop can be divided into 9 categories: AGV, automation equipment, product, laser engraved code, quality test, board, workshop environment, material storage, suspended chain. In the compressor pump body automatic assembly line, first, the industrial driver can automatically sense the current status of the AGV by wireless sensor networks. Then, the status data can be transmitted to the AGV scheduling App through industry network. Next, the nearest free AGV will be called to move the required material or tool to the work station through the communication of the industrial platform. When the material to be processed arriving the work position, the QR 7
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Fig. 6. Compressor assembly integrated information chain.
Fig. 7. Interoperable application lake.
4.2. Digital thread throughout the product lifecycle
body are computed, the corresponding instruction such as NC program or PLC program will be generated and sent to the machining tool to control its running status, processing routes or parameters for performing the processing task. During the processing stage, the current status of the storage, logistics and machine tools (operating state, running parameters, and task progress, etc.) are real-time monitored by the industry driver, which provide deep insight to visualize the manufacturing state in the Ind-OS. When the current batch of the compressor pump body is monitored to be nearly finished, the industry driver of a machining robot will interoperate across the services to call the driver of an AGV to transport the next batch of material to be processed to the working position.
After deploying the industry driver in compressor manufacturing workshop, the manufacturing process data captured from all processes is interconnected to build the whole digital thread throughout the compressor product lifecycle, which is the blood vessel of the Ind-OS. As shown in Fig. 5, the digital resource chain is an integration of all resource units in the compressor assembly line, which is easy to be managed and reconfigured when the resource units changed with the business change. It is composed of material storage, AGV, automation equipment, product/component, laser spray code machines, quality test, workshop environment and workers. In each branch, the corresponding industry driver, attributes data, and real-time data are chained with the essential resource units in the compressor assembly processing. Meanwhile, as shown in Fig. 6, the compressor assembly 8
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HCM, ERP and SRM are developed through the shared mode. Most of them are joined from some common micro-service components, which can also provide independent service. Therefore, in the real operation environment, the applications can interoperate together to collaboratively perform the compressor production task. Due to its ``Jigsaw" characteristic, users can quickly recombine a new application from a variety of micro-service components in application lake when the business changes or research requirements, such as the LIMS, Trial-MES and Think Tank. In the compressor pump body automatic assembly process, which is achieved by the cooperation of robots, machining tool, AGV, MES, PCS and PLC, is carried out in Fig. 8. At the beginning of the compressor pump body assembly process, the MES calls the RCS to transport the required components to the work station by AGVs. When the material to be processed arriving the work position, the RCS tells the MES to change the state of the process. Then, the PCS calls the PLC to automatically scan the QR codes attached to the components and upload the information to MES. According to this data, the assembly parameters such as machining position and torque for each pump body will be computed by MAS. Next, instruction is generated through the PCS to command the PLC of robots to carry out the assembly operation. After the current batch of compressor pump body finished, the MES will automatically call the AGV to transport the tray to the designated area. When the AGV arrives in front of the equipment grating door, the information interaction between the RCS and MES requests the grating door to be closed and opened, ensuring efficient, fast, safe and orderly production on site.
Table 1 The abbreviations of application system appeared in this case. Serial number
Abbreviations
Description
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
AGV PLC Device RCS PCS WMS MES QMS LMS LIMS EMS EHS PLM HCM ERP SRM HR CRM MAS FAS KDA
Automated Guided Vehicle Programmable Logic Controller Production Equipment Operation System Robot Control System Process Control System Warehouse Management System Manufacturing Execution System Quality Management System Labor Management System Laboratory Management System Energy Management System Environment Health System Product Lifecycle Management Human Capital Management Enterprise Resource Planning Supplier Relationship Management Human Resource Customer Relationship Management Manufacture Analysis System Finance Analysis System Knowledge Data Analysis
integrated information chain is organized throughout the compressor assembly lifecycle. In each work step, the resource units that need to be used are employed and linked to the corresponding process. For example, in order to achieve above “3C-3I” function of industry driver to collaboratively accomplish the task of pump assembly process, the data of material storage is provided to the AGV to transfer the nearest material. Further, the data of components are provided to the industry driver to compute the optimized the assembly parameters and command the assembly robot to complete the corresponding operation. All of the above operations are based on the digital thread interconnected the dispersed metadata.
5. Discussion The proposed Ind-OS is compared with the currently famous IIPs of GE Predix [40], ABB Ability [41], Siemens MindSphere [5] and PTC ThingWorx [6] from the view of connection, computing, integration and APPs development. As is suggested by Table 2, the proposed Ind-OS can provided more flexible connection mode for resource units, fogcloud computing mode and reconfigurable industrial applications.
4.3. Interoperable application lake
5.1. The impact on computing mode
The third step is the collaborative application development, which is the carrier of all assembly business. According to the business requirements of this compressor manufacturing company, the related application software divided into six layers are developed through the proposed four design modes. As shown in Fig. 7 and Table 1, the LMS, HR and CRM are developed through the single mode. The MAS, FAS and KDA are developed through the aggregator mode. The AGV, RCS, WMS, and PLM are developed through the chained mode. The PLM,
With the current EISs architecture, all the data are transmitted to the cloud platform and analyzed on the EIS servers. In manufacturing systems, some computing tasks are time sensitive. For example, the photograph of fabric should be analyzed within 100 ms to figure out the control signal of a knitting machine. In such scenarios, the cloud-based computing model can hardly meet the requirements of time latency. In
Fig. 8. Collaborative operation during compressor pump body automatic assembly. 9
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Customized
Customized
Open partner ecosystems
NavigateView (Internal and external users can actively participate in the development of the key functions) Jigsaw APPs
Departments of inner-enterprise
Product life cycle
Product life cycle & Internal enterprises chain
Product life cycle
Cloud computing
Fog-cloud computing
Cloud-based Internet of things computing Fog-cloud computing
5.2. The impact on industrial ecosystem In the current manufacturing systems, different EISs are isolated or weakly connected with each other, since the lack of standardization of resource units and mutual recognition of meta-data. With a larger view, the enterprises on the upstream and downstream of an industrial ecosystem can hardly be connected together for the more effective supply chain. In the proposed Ind-OS, the digital thread standardizes and links all the resource units together from various disciplines through the digital resource chain and the integrated information flow with the goal of enabling tasks across traditionally isolated disciplines. For example, the data of material, machine status, work-in-process, processing parameters can be correlated analyzed to improve the product quality. Further, by connecting all digital threads of the enterprises on the supply chain together, the data, information and knowledge can be accessed end-to-end on the Ind-OS, which finally results in a more agile and effective industrial ecosystem. 5.3. The impact on enterprise informationization
Fog-cloud computing
During the implementation of EISs, there are a lot of customized developing according to the individualized requirements of different enterprises, which leads to high industrial software cost. In the proposed Ind-OS, the huge EISs are decoupled into massive micro-services, and the users can select suitable micro-services to form "Jigsaw APPs" to meet the industrial requirements. Correspondingly, developers can release and upgrade the micro-services applications on the platforms. With the micro-service architecture in Ind-OS, the EISs are closer to the small-business, since the industrial software cost can be reduced by the decreasing of customized developing. Furthermore, the users can recombine the "Jigsaw APPs" quickly to cope with the new requirements of manufacturing systems, which is critical for quick transformation and upgrading of enterprises with an uncertain market. 6. Conclusion In this paper, an IIP framework called Ind-OS is proposed, which is made up of an edge layer with industry drivers, an industrial PaaS layer with digital thread, and an interoperable application layer. The proposed Ind-OS is a systematic framework focusing on the operation of manufacturing systems, which is likely to become the next generation of IIP for manufacturing systems. Compared with the current cloud-based EISs architecture, the IndOS can provide collaborative services from computing mode, industrial ecosystem, and industrial App. 1) In the edge layer of the Ind-OS, the industrial driver has the function of “3C-3I” structures: communication, computation, control, identification, insight and interoperation, which is adopted to realize the "plug and play" of resource units to the industrial internet platform. With the computation, insight, and interoperation services, the complex computing tasks can be finished with a collaborative scheme. The large tasks with huge computational cost can be
Ind-OS
PTC ThingWorx
Siemens MindSphere
ABB Ability
Fixed deployment (Wide connection protocols) Fixed deployment (Wide connection protocols) Fixed deployment (MindSphere connection protocol) Fixed deployment (End-to-end integration IIOT) Plug-and-play (Industrial driver) GE Predix
the Ind-OS, the computing is further extended to the edge of the platform. Lightweight time-sensitive tasks can be finished in the fog-edges by industrial drivers, and complex tasks with huge computational cost can be conducted on the platform. Moreover, with the designed digital thread connecting all resource units and meta-data, the global production information even in different APPs can be integrated to realize collaborative computing of multi-tasks. As a result, faster responses, better optimization and control can be achieved with the industrial driver. Compared with the cloud-based computing model in the current EISs architecture, the Ind-OS can provide multi-scaled computing service in the fog-cloud architecture, to empower the complex computing tasks with the collaborative scheme.
Product life cycle & Enterprises across industrial chain
APPs development mode Integrated mode Computing mode Connection mode IIPs
Table 2 Property comparison about the IIPs.
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conducted on the powerful cloud computing platform, and the urgent task with small computational cost and low latency can be finished by industrial drivers directly. 2) Different from the current IIPs, the PaaS layer in the Ind-OS is equipped with the digital thread, which consists of a digital resource chain and an integrated information chain. During the construction of the digital thread, the resource units are standardized and the meta-data in the EISs are authenticated. The industrial systems can be easily reconstructed and reconfigured for the uncertain issues. As a result, the physical resource units and cyber software applications are tightly collaborated together with to achieve higher production efficiency. 3) In the application layer, the industrial applications are decoupled into different micro-services in the application lake. With the industrial driver and digital thread, the function of each EIS can be realized through the interoperation of different micro-services with four modes: single mode, aggregator mode, chained mode and shared mode. With the proposed application lake, the suitable micro-services can be collaborated to form a “Jigsaw APPs” to meet the industrial requirements, and the “Jigsaw APPs” can be updated quickly to cope with the new requirements of manufacturing systems.
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At present, the IIP is still in its infancy. In future, we will consider to construct a mini Ind-OS platform in different particular production systems, such as wafer manufacturing system, welding production system. The second limitation lies in the implementation of the proposed Ind-OS, which will be programmed and embedded in the enterprise systems. In the future, manufacturing systems with a comprehensive domain model will be conducted. Declaration of Competing Interest The authors promise that there is no conflict of interest about this paper. Acknowledgements This work was supported by the Program of the National Natural Science Foundation of China under Grant nos. 51905091 and 51435009, and Sailing Program of Shanghai Science and Technology Committee under Grant no. 19YF1401500. References [1] N. Jazdi, Cyber physical systems in the context of industry 4.0, IEEE International Conference on Automation, 2014. [2] J. Lee, B. Bagheri, H.A. Kao, A cyber-physical systems architecture for industry 4.0based manufacturing systems, Manuf. Lett. 3 (2015) 18–23. [3] H. Panetto, et al., New perspectives for the future interoperable enterprise systems, Comput. Ind. 79 (C) (2016) 47–63. [4] Zhang, Y., et al., Long/short-term utility aware optimal selection of manufacturing service composition towards industrial internet platform. IEEE Transactions on. Industrial Industr. Informatics. [5] U. Lichtenthaler, Substitute or synthesis: the interplay between human and artificial intelligence, Res.-Technol. Manag. 61 (5) (2018) 12–14. [6] T. Fernándezcaramés, et al., A fog computing based cyber-physical system for the automation of pipe-related tasks in the industry 4.0 shipyard, Sensors 18 (6) (2018) 1961. [7] M. Wollschlaeger, T. Sauter, J. Jasperneite, The future of industrial communication: automation networks in the era of the internet of things and industry 4.0, IEEE Industr. Electron. Mag. 11 (1) (2017) 17–27. [8] W. Shao, et al., A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances for hypoid gear product, Robot. Comput. Integr. Manuf. 54 (2018) 1–16. [9] K. Menon, H. Kärkkäinen, J.P. Gupta, Role of industrial internet platforms in the management of product lifecycle related information and knowledge, Ifip International Conference on Product Lifecycle Management, 2016. [10] S. Al-Rubaye, et al., Industrial internet of things driven by SDN platform for smart grid resiliency, IEEE Int. Things J. (2017) PP(99): p. 1-1. [11] I. Seruca, et al., Proceedings of the 6th international conference on enterprise information systems (ICEIS 2004), Microcirculat. Endothelium Lymphatics 4 (4)
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