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Distributed Maintenance: A Literature Analysis and Classification
9th IFAC Conference on Manufacturing Modelling, Management and 9th IFAC Conference on Manufacturing Modelling, Management and Control Available online at www.sciencedirect.com 9th Conference on Modelling, Control 9th IFAC IFAC Conference on Manufacturing Manufacturing Modelling, Management Management and and Berlin, Germany, August 28-30, 2019 Control Berlin, ControlGermany, August 28-30, 2019 Berlin, Berlin, Germany, Germany, August August 28-30, 28-30, 2019 2019
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IFAC PapersOnLine 52-13 (2019) 619–624
Distributed Maintenance: A Literature Analysis and Classification Distributed Maintenance: A Literature Analysis and Classification Distributed Maintenance: A Literature Analysis and Classification Ali Attajer Saber Darmoul Fouad Riane Abdelghani Bouras
Ali Attajer Saber Darmoul Fouad Riane Abdelghani Bouras [email protected]@[email protected]@centraleAli Saber Darmoul Fouad Abdelghani Bouras Ali Attajer Attajer Saber Darmoul Fouad Riane Riane Abdelghani Bouras [email protected]@[email protected]@centralecasablanca.ma casablanca.ma casablanca.ma casablanca.ma [email protected]@[email protected]@[email protected]@[email protected]@centralecasablanca.ma casablanca.ma casablanca.ma casablanca.ma casablanca.ma casablanca.ma casablanca.ma Bouskoura Ville Verte, 27182, Casablanca, Moroccocasablanca.ma casablanca.maEcole Centrale Casablanca, casablanca.ma casablanca.ma casablanca.ma Ecole Centrale Casablanca, Bouskoura Ville Verte, 27182, Casablanca, Morocco Centrale Casablanca, Bouskoura Verte, 27182, Morocco Laboratoire Génie Ecole Industriel (LGI), EA 2606, CentraleSupélec, Joliot-Curie, 91190, Paris-Saclay Ecole Centrale Casablanca, Bouskoura Ville Ville Rue Verte, 27182, Casablanca, Casablanca, Morocco (Gif-sur-Yvette), Laboratoire Génie Industriel (LGI), EA 2606, CentraleSupélec, France Rue Joliot-Curie, 91190, Paris-Saclay (Gif-sur-Yvette), Laboratoire Laboratoire Génie Génie Industriel Industriel (LGI), (LGI), EA EA 2606, 2606, CentraleSupélec, CentraleSupélec, Rue Joliot-Curie, Joliot-Curie, 91190, 91190, Paris-Saclay Paris-Saclay (Gif-sur-Yvette), (Gif-sur-Yvette), France Rue France France Abstract: Recent technological advances, such as Wireless Sensor and Ad-hoc Networks, Internet of Abstract: Recent advances, such as Wireless and Ad-hoc Networks, Internetand of Things (IoT), Cloudtechnological Computing, multi-agent systems, etc. led Sensor to the emergence of control paradigms Abstract: Recent technological advances, such such as Wireless Wireless Sensor and Ad-hoc Ad-hoc Networks, Internetand of Abstract: Recent advances, as and Networks, Internet of Things (IoT), Cloudtechnological Computing,processing multi-agent systems, etc. led Sensor to are thedistributed/decentralized emergence of control paradigms architectures, where information and decision making over several Things (IoT), (IoT), where Cloud information Computing,processing multi-agentand systems, etc. led to to are thedistributed/decentralized emergence of of control control paradigms paradigms and Things Cloud Computing, multi-agent systems, etc. led the emergence and architectures, decision making over several smart production objects (machines, robots, products, etc.). Indeed, a growing research effort focused on architectures, where information processing and decision making are distributed/decentralized distributed/decentralized over several architectures, where information processing decision making are over several smart production objects (machines, robots,and products, etc.). Indeed, a growing research effort focused on developing distributed control architectures, where production equipment (machines, robots, etc.) are smart production objects (machines, robots, products, etc.). Indeed, aa growing research effort focused on smart production objects (machines, robots, products, etc.). Indeed, growing research effort focused on developing distributed control architectures, where production equipment (machines, robots, etc.) are endowed with some kind of intelligence to carry production planning, control, and maintenance developing distributed control architectures, where production equipment (machines, robots, etc.) are developing distributed control architectures, where production equipment (machines, robots, etc.) are endowed with some kind of intelligence to carry production planning, control, and maintenance engineering tasks. However, only a limited number of works focused on developing smart/intelligent endowed with some kind intelligence to carry ofproduction planning, control, and maintenance endowed approaches, with some kind of ofonly intelligence planning, control, and maintenance engineering tasks. However, a limited number works on developing smart/intelligent product where products play to an carry activeproduction role infocused the decision processes. Within the engineering tasks. However, only a limited number of works focused on developing smart/intelligent engineering tasks. However, only a limited number of works focused on developing smart/intelligent product approaches, where products play an active role in the decision processes. Within with the smart/intelligent product community, only a few works are interested in endowing products product where products play active role the decision processes. Within the product approaches, approaches, products play with anfew active rolearein inand the problems decision processes. Within with the smart/intelligent community, aan works interested in endowing capabilities that product makewhere them able to only deal disturbances related toproducts maintenance smart/intelligent product community, only a few works are interested in endowing products with smart/intelligent product community, only a few works are interested in endowing products with capabilities that themmaintenance able to deal with disturbances related to maintenance engineering, and tomake influence decisions and strategiesand in anproblems autonomous, adaptive and resilient capabilities that that make themmaintenance able to to deal deal with disturbances disturbances and problems related to maintenance maintenance capabilities able with related to engineering, and tomake influence decisions inmaintenance anproblems autonomous, adaptive and resilient way. This position paperthem investigates literature to and shedstrategies light onand distribution possibilities. engineering, and to to influence influence maintenance decisions and strategies inmaintenance an autonomous, autonomous, adaptive possibilities. and resilient resilient engineering, and maintenance decisions and strategies in an adaptive and way. This position paper investigates literature to shed light on distribution Important questions are addressed, such as: What kinds of information and decisions are distributed in way. This position paper investigates literature to shed light on maintenance distribution possibilities. way. This position paper investigates literature to shed light on maintenance distribution possibilities. Important questions are addressed, such as: What kinds of information and decisions are distributed in maintenance engineering problems? How is such distribution achieved so far? What active roles do Important questions are addressed, such as: What kinds of information and decisions are distributed in Important questions are addressed, such as: What kinds of information and decisions are distributed in maintenance engineering problems? How is such distribution achieved so far? What active roles do smart/intelligent production objects, especially products, endorse in such architectures? What are the maintenance engineering problems? How is such distribution achieved so far? What active roles do maintenance engineering problems? How is such distribution achieved so far? What active roles do smart/intelligent production objects, especially products, endorse in such architectures? What are the benefits of such distribution, and how does it impact maintenance tasks in smart factories of the future? smart/intelligent production objects, especially products, endorse in such architectures? What are smart/intelligent production objects, especially products, endorse tasks in that such architectures? What are the the benefits of such and howand does it impact maintenance inare smart of the future? And finally, whatdistribution, are the challenges future research perspectives stillfactories worth investigating to benefits of such distribution, and how does it impact maintenance tasks in smart factories of the future? benefits of such distribution, and how does it impact maintenance tasks in smart factories of the future? And finally, what are the challenges and future research perspectives that are still worth investigating to foster developments in this field? Copyright © 2019 IFAC And finally, what are areinthe the challenges and future future research perspectives that that are are still still worth worth investigating investigating to to And what and research perspectives fosterfinally, developments thischallenges field? Copyright © 2019 IFAC Keywords: Distribution, Maintenance engineering, Classification, Smart/Intelligent foster developments in this field? Copyright © 2019 IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.Products,Architecture. All rights reserved. foster developments in this field? Copyright © 2019 IFAC Keywords: Distribution, Maintenance engineering, Classification, Smart/Intelligent Products,Architecture. Keywords: Maintenance engineering, engineering, Classification, Classification, Smart/Intelligent Smart/Intelligent Products,Architecture. Products,Architecture. Keywords: Distribution, Distribution, Maintenance 1. INTRODUCTION products with capabilities to make them able to deal with 1. INTRODUCTION products withandcapabilities to make them able to deal with disturbances problems related to maintenance engineering, 1. INTRODUCTION products with to them able to deal Recent years have a rapid development of 1. witnessed INTRODUCTION products withandcapabilities capabilities to make make themand able to deal inwith with disturbances problems related to maintenance engineering, and to influence maintenance decisions strategies an Recent years have witnessed a rapidSensor development of disturbances and problems related to maintenance engineering, innovative technologies, such as Wireless and Ad-hoc disturbances and problems related to maintenance engineering, and to influence maintenance decisions and strategies in an Recent years have witnessed a rapid development of autonomous, adaptive and resilient way (Andreadis, Bouzakis, Recent years have witnessed a rapidSensor development of innovative technologies, such as Wireless and Ad-hoc and to influence maintenance decisions and strategies in an Networks, Internet of Things (IoT), Cloud Computing, etc. and to influence maintenance decisions and strategies in an autonomous, adaptive and resilient way (Andreadis, Bouzakis, innovative technologies, such as Wireless Sensor and Ad-hoc et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et innovative technologies, such as Wireless Sensor and Networks, Internet Things (IoT), Cloud Computing, etc. autonomous, adaptive and resilient way (Andreadis, Bouzakis, (Zhong et al., 2017).ofThese technologies enabled newAd-hoc trends, autonomous, adaptive and resilient way (Andreadis, Bouzakis, et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et Networks, Internet of Things (IoT), Cloud Computing, etc. al., 2014). Although the rise of the Industry 4.0 paradigm is Networks, Internet of Things (IoT), Cloud Computing, etc. (Zhong al., 2017).(Wang, These technologies enabled et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et such as etubiquitous Ong and Nee, 2018)new andtrends, smart et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et al., 2014). Although the rise of the Industry 4.0 paradigm is (Zhong etubiquitous al., 2017). 2017).(Wang, These technologies technologies enabled new trends, stimulating a burst of research (Lu, 2017), only a few (Zhong et al., These enabled new trends, such as Ong and Nee, 2018) and smart al., 2014). Although the rise of the Industry 4.0 paradigm is (Kusiak, 2018) manufacturing, and allowed developing new al., 2014). Although the rise of the Industry 4.0 paradigm is stimulating a burst of research (Lu, 2017), only a few such as as ubiquitous ubiquitous (Wang, Ong Ongand andallowed Nee, 2018) 2018) and smart smart researchers discuss the research impacts (Lu, of 2017), such technological such (Wang, and Nee, and (Kusiak, 2018) manufacturing, developing new stimulating a burst of only a few control architectures, based on multi-agent, holonic and stimulating a burst of research (Lu, 2017), only a few researchers discuss the impacts of such technological innovations on maintenance practices, architectures and (Kusiak, 2018) manufacturing, and allowed developing new (Kusiak,architectures, 2018) manufacturing, andmulti-agent, allowed developing control based on holonic new and researchers discuss the of technological bionic paradigms (Darmoul, Pierreval and Hajri–Gabouj, researchers discuss the impacts impacts of such such technological innovations on maintenance practices, architectures and control architectures, based multi-agent, holonic techniques. More generally, maintenance distribution choices control paradigms architectures, based on on multi-agent, holonic and and bionic (Darmoul, Pierreval and Hajri–Gabouj, innovations on maintenance practices, architectures and 2013). The main objective is to distribute the information innovations on maintenance practices, architectures and techniques. More generally, maintenance distribution choices bionic paradigms (Darmoul, Pierreval and Hajri–Gabouj, raise fundamental questions about maintenance organization, bionic The paradigms (Darmoul,is Pierreval andthe Hajri–Gabouj, 2013). main objective to distribute information techniques. More generally, maintenance distribution choices processing and to decentralize decision making over several techniques. More generally, maintenance distribution choices raise fundamental questions about maintenance organization, 2013). main objective information planning and operation that were not sufficientlyorganization, emphasized 2013). The Theand main objective is is to to distribute distribute the the over information processing to decentralize several raise questions about maintenance entities forand more flexibility, decision autonomymaking and resilience to raise fundamental fundamental questions aboutnot maintenance planning and operation that were sufficientlyorganization, emphasized processing to decentralize decision making over several and discussed in literature. processing and to decentralize decision making over several entities for more flexibility,and autonomy resilience to planning and operation that were not sufficiently emphasized change (Bayar, Hajri-Gabouj Darmoul,and 2018). planning and operation that were not sufficiently emphasized and discussed in literature. entities for more more flexibility,and autonomy and resilience to to entities for flexibility, autonomy resilience change (Bayar, Hajri-Gabouj Darmoul,and 2018). and discussed in literature. To fill in this gap, this position paper investigates literature to and discussed in literature. change Hajri-Gabouj Darmoul, 2018). Indeed, a growing research and effort focused on endowing change (Bayar, (Bayar, Hajri-Gabouj and Darmoul, 2018). To fill in this gap, this position paper investigatespossibilities, literature to shed light on maintenance distribution Indeed, a growing research effortrobots, focused on with endowing To fill in this gap, this position paper investigates literature production equipment (machines, etc.) some To fill in this gap, this position paper investigates literature to to shed light on maintenance distribution possibilities, Indeed, research effort focused on endowing opportunities and maintenance challenges. Important questions are Indeed, aa growing growing research effortrobots, focused on with endowing production equipment (machines, etc.) some shed light on distribution possibilities, kind of intelligence to carry production planning, control, and shed light on maintenance distribution possibilities, opportunities and challenges. Important questions are production equipment (machines, robots, etc.) with some addressed, such and as: What kinds of information and decisions are production equipment (machines, etc.) control, with some kind of intelligence to carry production and opportunities challenges. Important questions are maintenance engineering tasks (Giretrobots, etplanning, al., 2018). However, opportunities and challenges. Important and questions addressed, such as: What kinds of information decisions are kind of intelligence to carry production planning, control, and distributed in maintenance engineering problems? How is such kind of intelligence to carry production planning, control, and maintenance engineering (Giretfocused et al., 2018). However, addressed, such as: What kinds of information and decisions are only a limited number tasks of works on developing addressed, such as: What kinds of information and decisions are distributed in achieved maintenance problems? Howenabling is such maintenance engineering tasks (Giretfocused et al., al., 2018). 2018). However, distribution so engineering far in terms of both maintenance engineering tasks (Giret et However, only a limited number of works on developing distributed in maintenance engineering problems? How is such smart/intelligent product approaches, where products play an distributed in maintenance engineering problems? How is such distribution achieved so far in terms of both enabling only aa limited limited number of works works focused focused on developing developing technologies and controlsoarchitectures? What active roles do only of on smart/intelligent product approaches, where(Trentesaux products playand an distribution achieved far of both active role in number the decision processes distribution achieved far in in interms terms of active both enabling enabling technologies andproducts controlsoarchitectures? roles do smart/intelligent product approaches, where products play an smart/intelligent endorse suchWhat architectures? What smart/intelligent product approaches, where products play an active role in the decision processes (Trentesaux and technologies and control architectures? What active roles do Thomas, 2013; Aubry et al., 2016). Despite a growing technologies andof control architectures? What active roles do smart/intelligent products endorse in such architectures? What active role in the decision processes (Trentesaux and are the benefits such distribution, and how does it impact active role in the decision (Trentesaux and Thomas, 2013; Aubry et al., processes 2016). Despite a growing smart/intelligent products endorse in such architectures? What number of works on distributed maintenance (Upasani et al., smart/intelligent products endorse in such architectures? What are the benefits of such distribution, and how does it impact Thomas, et 2016). aa growing maintenance tasks in smart factories of the future? And finally, Thomas,of2013; 2013; Aubry et al., al., maintenance 2016). Despite Despite growing number works Aubry on distributed (Upasani al., are distribution, does it impact 2017), condition-based maintenance (CBM), andet on are the the benefits benefits of ofinsuch such distribution,ofand and how does it finally, impact maintenance smart the how future? And number of works on distributed maintenance (Upasani et al., what are the tasks challenges andfactories future research perspectives that number of works on distributed maintenance (Upasani 2017), condition-based maintenance (CBM), andet al., on maintenance tasks in smart factories of the future? And finally, Prognostics and Health Management (PHM) (Atamuradov et maintenance tasks in smart factories of the future? And finally, what are the challenges and future research perspectives that 2017), condition-based maintenance (CBM), and on are still worth investigating to foster developments in this field? 2017), condition-based maintenance (CBM), and on Prognostics and Health (Atamuradov et what are the challenges and future research perspectives that al., 2017), only a few Management works raise (PHM) the issue of endowing what are the challenges and future research perspectives that are still worth investigating to foster developments in this field? Prognostics and Health Health Management (PHM) (Atamuradov et Prognostics and (Atamuradov et al., 2017), only a few Management works raise (PHM) the issue of endowing are still worth investigating to foster developments in this field? are still worth investigating to foster developments in this field? al., only aa few works the of 2405-8963 © 2019, of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. al., 2017), 2017), only IFAC few(International works raise raiseFederation the issue issue of endowing endowing
Copyright 2019 responsibility IFAC 626Control. Peer review©under of International Federation of Automatic Copyright © 2019 IFAC 626 10.1016/j.ifacol.2019.11.089 Copyright 626 Copyright © © 2019 2019 IFAC IFAC 626
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Therefore, section 2 introduces maintenance organization and management in manufacturing systems. Section 3 introduces the main types of distribution architectures. Section 4 provides a classification of maintenance distribution possibilities. Section 5 positions the smart/intelligent product concept with respect to previously introduced control architectures and analyzes literature to bring answers to the previous research questions. Finally, section 6 discusses challenges and future research directions with respect to the industrial applications of smart/intelligent products in maintenance engineering. 2.
MAINTENANCE ORGANIZATION AND MANAGEMENT
Maintenance enables equipment, processes and engineering services to be available when required and ensures that the services retain their value as assets. Traditionally, maintenance activities have been carried out in a variety of ways by a variety of people. According to CIBSE (CIBSE, 2008), management of maintenance encompasses both ‘technical’ and ‘control’ aspects. The technical aspect includes determining what equipment is to be maintained, how and when; identifying problems and diagnosing causes; monitoring effects; preparing and analyzing records and technical information; initiating procedures to cope with situations before they arise; and ensuring that the chosen techniques are achieving the required results. The control aspect is aimed at minimizing the costs associated with the non-availability of an engineering service, and involves management of labor, spare parts and equipment to match the workload; locating where work is required; organizing transport; setting priorities; and coordinating action. It can extend to setting budgets, monitoring expenditure, identifying high maintenance cost plant and collecting information to form a basis for decision making. 3.
DISTRIBUTED ARCHITECTURES
Several classifications of distribution architectures were suggested and discussed in literature, with different levels of detail (Andreadis, Klazoglou, et al., 2014). The classification by Trentesaux (Trentesaux, 2009) is particularly interesting, mainly because of its conciseness and suitable level of abstraction. According to (Trentesaux, 2009), control architectures may be classified into centralized and noncentralized, this latter including fully hierarchical, fully heterarchical and semi heterarchical architectures (see Fig. 1). Control architecture
Centralized
Fully hierarchical
Noncentralized
Semi heterarchical Fully heterarchical
In centralized control, only one decision entity controls several resources (e.g. machines, robots, conveyors, etc.), activities (e.g. storage, processing, tool change, etc.), and/or processes (e.g. production, maintenance, quality inspection, etc.). For example, an industrial Programmable Logic Controller (PLC) is usually used to control a Flexible Manufacturing Cell (FMC), made of a machine, an automatic tool changer, a storage buffer, a conveyor and a robot for material handling. When the number of controlled entities is arbitrarily reduced, such as in this FMC example, this type of control leads to acceptable performance. However, a single controller at a factory level (e.g. a single PLC to control many FMCs) will face difficulties due to both realtime operational requirements (e.g. size and frequency of data streams, time lags between events and information processing, etc.), production system complexity (e.g. data acquisition, processing, communication, and storage, demand uncertainty, resource availability and reliability, etc.), and scalability (e.g. system upgrade, size improvements, new production capabilities, etc.). Decentralization of control is considered as a possible way to overcome these limitations. Two fundamental design choices guide the different ways to decentralize control decisions: using hierarchical or heterarchical relationships (Trentesaux, 2009). Hierarchical decentralization dates back to the early 1970s. Fully hierarchical systems are based exclusively on vertical, master-slave relationships between decision entities at different hierarchical control levels. Decision entities at lower hierarchical levels are closer to production resources and activities and provide information to decision entities at higher hierarchical levels. Based on this information, decision entities at higher hierarchical levels make decisions and control decision entities at lower hierarchical levels. Therefore, hierarchical decentralization consists in splitting the global control problem into hierarchically dependent sub-problems, each sub-problem being assigned to a decision level with decreasing time ranges (i.e., strategic, tactical and operational, such as planning, scheduling and supervision). This choice maintains sufficient long-term optimization, while supporting less short-term optimization (e.g., agility, reactivity). Although this approach has led to well-known and established information and production planning and control system architectures, such as the Manufacturing Resources Planning (MRP2) and the Enterprise Resource Planning (ERP), it is based on hard assumptions, such as the long-term availability and reliability of both supply and demand, the optimal behavior and high reliability of production resources, low product diversity, and the observability and controllability of all the possible internal variables (Trentesaux, 2009). In fully heterarchical control architectures, each decision entity can be seen as both a master and a slave, so that no hierarchy can be identified. Whereas hierarchy is a kind of “vertical” decentralization of control, heterarchy is a kind of “horizontal” decentralization of control. Decision entities have some degree of autonomy and work together to react quickly to disturbances instead of wasting response time lags to request control decisions from upper decision levels. Multi-agent systems is the
Fig. 1. Classification of distributed architectures. 627
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paradigm and technology usually used to implement fully heterarchical control (Shen et al., 2006; Leitão, 2009). In such control architectures, negotiation and cooperation appear as interaction processes in addition to coordination, which is the main (even unique) interaction mechanism in fully hierarchical architectures. However, negotiation and cooperation lead to new problems, for example, the need to prove deadlock avoidance mechanisms and more generally, the need to prove that sufficient level of performance can be attained (Trentesaux, 2009). The desire to integrate both hierarchical and heterarchical mechanisms into a distributed control system can be seen as an essential feature of semi heterarchical architectures, usually modelled and implemented through the holonic paradigm (Giret et al., 2018) in an attempt to benefit from the advantages of both approaches. 4.
DISTRIBUTED ARCHITECTURES FOR MAINTENANCE ENGINEERING
Although several researchers discussed the benefits of distributing maintenance activities and decision-making in manufacturing and service companies (Upasani et al., 2017), there is still a lack of classifications of maintenance distribution types and possibilities. We suggest such a classification in Fig. 2 to structure existing works on distributed maintenance. The classification shows four streams that capture the focuses of the current distributed architectures for maintenance, and that will be discussed in the following subsections.
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and reliability of the manufacturing facilities and engineering services and assets that contribute to adding value to manufactured products. In these references, several schemes were suggested to distribute maintenance information, activities and decision-making (see sections 4.2 to 4.4) on intelligent production objects (i.e. machines, products, etc.). On another hand, for products with high added value, high complexity (e.g. automobiles, airplanes, boats, submarines, satellites, etc.), which operate in harsh environments, and/or which are called to operate without human intervention (e.g. unmanned vehicles), a body of research focuses on developing self-monitoring, self-diagnosing, self-healing or self-repairing products, i.e. embedded systems that can perform some maintenance tasks on their own (Ghosh et al., 2007; McWilliam et al., 2018). However, it is worth noticing that, to the best of the authors’ knowledge, there is a lack of works that confer smart/intelligent products the capability to contribute to the maintenance of the system that manufactures them. 4.2. Functional distribution Standards from various organizations – such as the International Electro-technical Commission (IEC), the Institute of Electrical and Electronics Engineers (IEEE), the International Organization for Standardization (ISO), the Machinery Information Management Open Standards Alliance (MIMOSA), and the Society of Automotive Engineers (SAE) International – were suggested to structure maintenance functions, and to enable intelligent decision-making for improved performance, safety, reliability, and maintainability (Vogl, Weiss and Donmez, 2014). They cover standards for Condition Monitoring and Diagnostics (CM&D), Prognostics and Health Management (PHM), system development, data collection and analysis techniques, data management, system training, and software interoperability. Such standards guide and structure the design and development of functionally distributed maintenance approaches. For example, the OSACBM (Open System Architecture – Condition-Based Maintenance) standard (Sreenuch, Tsourdos and Jennions, 2013), released by MIMOSA, is dedicated to the development of conditional or provisional maintenance strategies, and can be adapted to each industrial need. It contains seven flexible modules, which content (methodology and algorithms) is configurable by the user. In their paper, Cachada et al. (Cachada et al., 2018) introduce the architecture of an intelligent, functionally distributed and predictive maintenance system, aligned with Industry 4.0 principles and following the structure of functional blocks of the OSA-CBM standard. 4.3. Geographical distribution
Fig. 2. Classification of maintenance distribution.
4.1. Distribution based on the targeted system This type of distribution appears as an answer to the question “what is to be maintained?”. Usually, researchers develop distributed maintenance approaches to ensure availability
With respect to the geographical position of the maintenance center relative to the production system, Karray et al. (Karray et al., 2009) discuss three possible distribution schemes: remote-maintenance, e-maintenance and s-maintenance. The remote-maintenance system consists of at least two computer systems that exchange data: a production management system that sends data to a maintenance management system, which 628
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analyzes transmitted data, and sends maintenance decisions back to the production management system. An emaintenance system is implemented on a cooperative distributed platform integrating different systems and maintenance applications of several production sites, such as in the European project PROTEUS (Bangemann et al., 2004). The s-maintenance architecture is intended to improve the performance of the e-maintenance architecture at the level of communication and data exchange between systems and allows considering data semantics and knowledge management (Matsokis et al., 2010). 4.4. Metaphor based distribution Metaphor based distribution stems from the fundamental idea that natural, biological, and social systems are able to perform complex tasks and to achieve robust and resilient performance while relying on simple and effective mechanisms. This idea provides instructive sources of inspiration for scientists to identify those architectures, concepts and mechanisms and develop adaptive systems that mimic them in order to be able to evolve and to deal with the new challenges facing manufacturing systems (Leitão, Barbosa and Trentesaux, 2012). Three paradigms are streaming search on metaphor based distribution: multiagent, holonic and bionic systems. These paradigms provide ways to identify control layers and decisional entities on which information will be distributed and decision making will be decentralized. A multi-agent system (MAS) is a computerized selforganized system composed of multiple interacting intelligent agents. Intelligence may include methodic, functional, procedural approaches, algorithmic search or machine learning. Several applications of MAS for distributed maintenance purposes are discussed in the literature. Gallab et al. (Gallab et al., 2017) provide a recent overview of existing research applying MAS to maintenance topics. The holonic concept originated from the work of Hungarian author and philosopher Arthur Koestler who tried to capture the behavior of complex systems by considering its constituent entities as being both wholes and parts at the same time (Babiceanu and Chen, 2006). Holonic control aims to merge the best properties of both hierarchical and heterarchical systems, namely, a high and predictable performance with a high robustness against disturbances and unforeseen changes. Holonic control is implemented using MAS. Therefore, advances in MAS theory and applications are beneficial to future development and enhancement of holonic systems. Giret et al. (Giret et al., 2018) provide a recent overview of holonic control applications in manufacturing systems. Biologically inspired engineering design uses analogies to biological systems to develop solutions for engineering problems. Leitão et al. (Leitão et al., 2012) provide an overview of some of the principles found in nature and biology and analyze their effectiveness to enhance multi-
agent systems to solve complex manufacturing engineering problems. However, they do not mention biological immunity as a trendy source of inspiration. Bayar et al. (Lee et al., 2011) fill in this gap and provide a review on fault detection, diagnosis and recovery using Artificial Immune Systems. Lee et al. (Lee et al., 2011) discuss research in the areas of distributed selfmaintenance for smart machines in manufacturing systems using Engineering Immune Systems (EIS). Fasanotti et al. (Fasanotti et al., 2016) describe a hybrid immune-MAS architecture, which aims to overcome the limitations of current predictive and preventive maintenance systems when they are applied to geographically widespread systems, such as in the cases of oil transfer systems via pipelines and wastewater treatment systems. 5.
PRODUCT INTELLIGENCE IN MAINTENANCE ENGINEERING
Several definitions and classifications of product intelligence were suggested and discussed in the literature, with different levels of detail (Meyer, Framling and Holmstrom, 2009; Främling et al., 2011). According to Porter and Heppelmann (Porter and Heppelmann, 2014), the capabilities of smart/intelligent products relative to maintenance engineering can be grouped into: monitoring, optimization, and autonomy. Monitoring refers to using sensors and external data sources to enable alerts and notifications of changes. Monitoring capabilities enable optimization of product operation and use in order to allow predictive diagnostics, service, and repair. Combining monitoring and optimization allows self-diagnosis and self-maintenance. Brahim-djelloul et al. (Brahim-djelloul et al., 2012) investigated the ability of products to trigger alerts following a break in the cold chain during transportation using RFID tags and temperature sensors integrated in the product packaging. The SURFER project (Mortellec et al., 2013) develops a holonic cooperative fault diagnosis approach, along with a generic architecture, to increase the embedded diagnosis capabilities of complex transportation systems. This concept is applied to the fault diagnosis of door systems of a railway transportation system. SURFER involves three types of holons: door holons, vehicle holons, and train holon. The diagnostic generated by the train holon is sent to the maintenance center, so that it can plan maintenance interventions. Lee et al. (Lee et al., 2014) suggest a Watchdog Agent embedded in a smart/intelligent product. Such an agent relies on sensors and computational prognostic algorithms to forecast future performance degradation and diagnose the reasons for degradation through trending and statistical modeling of the observed process signatures. With respect to self-maintenance systems, Takata et al. (Takata et al., 2004) identify three types of technologies: control, function redundancy, and network/group intelligence. A self-maintenance system is equipped with a control and reasoning processor, sensors, and actuators to perform these tasks. In case of failure, the smart/intelligent product can autonomously recognize the reasons for failure and try to reconfigure its state and its behavior to maintain its function (Urnes and Yeager, 1991). 629
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6.
DISCUSSION AND CONCLUSION
This paper suggested a classification of maintenance distribution possibilities and analyzed literature with respect to distributed maintenance architectures in general, and the role of active products in maintenance engineering in particular. The suggested classification is based on four streams that allow collecting the focuses of the current distributed architectures for maintenance. From this analysis, it appears that there is a lack of references of distributed maintenance architectures based on smart/intelligent products. To the best of the authors’ knowledge, there is no work that deals with smart/intelligent products contributing to maintain the system that manufactured them. To the question why such emergent distributed approaches are not adopted by industry, Leitão (Leitão, 2009) points out that, although the answer is not clear and unique, two groups of reasons can be identified: conceptual efficiency in the paradigms, and development-related aspects. Undoubtedly, the implementation of such approaches will be boosted by new ICT technologies, namely blockchain, advanced data analytics and augmented reality (Fernández et al., 2018). With the advent of Internet of Things, manufacturing systems and experts are witnessing a kind of transfer of responsibility from Man to intelligent objects. In traditional decision-making processes, it was the maintainer who used to reason, analyze and decide about the appropriate maintenance interventions. But with the advent of Internet of Things, smart products and smart objects in general are more and more able to self-monitor, self-diagnose, generate alerts and make decisions. The role of the maintainer is slowly moving from making maintenance decisions, to supervising, evaluating and selecting from sets of alternatives made by smart production objects. The main challenge is to design the distributed architecture that supports, enhances and enables the use of these technologies to the best of their potential and promises. REFERENCES Andreadis, G., Klazoglou, P., et al. (2014) ‘Classification and review of multi-agents systems in the manufacturing section’, Procedia Engineering. Elsevier B.V., 69, pp. 282– 290. doi: 10.1016/j.proeng.2014.02.233. Andreadis, G., Bouzakis, K., et al. (2014) ‘Review of AgentBased Systems in the Manufacturing Section’, Universal Journal of Mechanical Engineering, 2(2), pp. 55–59. Atamuradov, V. et al. (2017) ‘Prognostics and Health Management for Maintenance Practitioners - Review , Implementation and Tools Evaluation’, International Journal of Prognostics and Health Management, 8. Aubry, A. et al. (2016) ‘Product driven systems facing unexpected perturbations: How operational research models and approaches can be useful?’, in Studies in Computational Intelligence, 6th workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, SOHOMA 2016. Springer, pp. 259–267. doi: 10.1007/978-3-319-51100-9_23.
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Distributed Maintenance: A Literature Analysis and Classification Distributed Maintenance: A Literature Analysis and Classification Distributed Maintenance: A Literature Analysis and Classification Ali Attajer Saber Darmoul Fouad Riane Abdelghani Bouras
Ali Attajer Saber Darmoul Fouad Riane Abdelghani Bouras [email protected]@[email protected]@centraleAli Saber Darmoul Fouad Abdelghani Bouras Ali Attajer Attajer Saber Darmoul Fouad Riane Riane Abdelghani Bouras [email protected]@[email protected]@centralecasablanca.ma casablanca.ma casablanca.ma casablanca.ma [email protected]@[email protected]@[email protected]@[email protected]@centralecasablanca.ma casablanca.ma casablanca.ma casablanca.ma casablanca.ma casablanca.ma casablanca.ma Bouskoura Ville Verte, 27182, Casablanca, Moroccocasablanca.ma casablanca.maEcole Centrale Casablanca, casablanca.ma casablanca.ma casablanca.ma Ecole Centrale Casablanca, Bouskoura Ville Verte, 27182, Casablanca, Morocco Centrale Casablanca, Bouskoura Verte, 27182, Morocco Laboratoire Génie Ecole Industriel (LGI), EA 2606, CentraleSupélec, Joliot-Curie, 91190, Paris-Saclay Ecole Centrale Casablanca, Bouskoura Ville Ville Rue Verte, 27182, Casablanca, Casablanca, Morocco (Gif-sur-Yvette), Laboratoire Génie Industriel (LGI), EA 2606, CentraleSupélec, France Rue Joliot-Curie, 91190, Paris-Saclay (Gif-sur-Yvette), Laboratoire Laboratoire Génie Génie Industriel Industriel (LGI), (LGI), EA EA 2606, 2606, CentraleSupélec, CentraleSupélec, Rue Joliot-Curie, Joliot-Curie, 91190, 91190, Paris-Saclay Paris-Saclay (Gif-sur-Yvette), (Gif-sur-Yvette), France Rue France France Abstract: Recent technological advances, such as Wireless Sensor and Ad-hoc Networks, Internet of Abstract: Recent advances, such as Wireless and Ad-hoc Networks, Internetand of Things (IoT), Cloudtechnological Computing, multi-agent systems, etc. led Sensor to the emergence of control paradigms Abstract: Recent technological advances, such such as Wireless Wireless Sensor and Ad-hoc Ad-hoc Networks, Internetand of Abstract: Recent advances, as and Networks, Internet of Things (IoT), Cloudtechnological Computing,processing multi-agent systems, etc. led Sensor to are thedistributed/decentralized emergence of control paradigms architectures, where information and decision making over several Things (IoT), (IoT), where Cloud information Computing,processing multi-agentand systems, etc. led to to are thedistributed/decentralized emergence of of control control paradigms paradigms and Things Cloud Computing, multi-agent systems, etc. led the emergence and architectures, decision making over several smart production objects (machines, robots, products, etc.). Indeed, a growing research effort focused on architectures, where information processing and decision making are distributed/decentralized distributed/decentralized over several architectures, where information processing decision making are over several smart production objects (machines, robots,and products, etc.). Indeed, a growing research effort focused on developing distributed control architectures, where production equipment (machines, robots, etc.) are smart production objects (machines, robots, products, etc.). Indeed, aa growing research effort focused on smart production objects (machines, robots, products, etc.). Indeed, growing research effort focused on developing distributed control architectures, where production equipment (machines, robots, etc.) are endowed with some kind of intelligence to carry production planning, control, and maintenance developing distributed control architectures, where production equipment (machines, robots, etc.) are developing distributed control architectures, where production equipment (machines, robots, etc.) are endowed with some kind of intelligence to carry production planning, control, and maintenance engineering tasks. However, only a limited number of works focused on developing smart/intelligent endowed with some kind intelligence to carry ofproduction planning, control, and maintenance endowed approaches, with some kind of ofonly intelligence planning, control, and maintenance engineering tasks. However, a limited number works on developing smart/intelligent product where products play to an carry activeproduction role infocused the decision processes. Within the engineering tasks. However, only a limited number of works focused on developing smart/intelligent engineering tasks. However, only a limited number of works focused on developing smart/intelligent product approaches, where products play an active role in the decision processes. Within with the smart/intelligent product community, only a few works are interested in endowing products product where products play active role the decision processes. Within the product approaches, approaches, products play with anfew active rolearein inand the problems decision processes. Within with the smart/intelligent community, aan works interested in endowing capabilities that product makewhere them able to only deal disturbances related toproducts maintenance smart/intelligent product community, only a few works are interested in endowing products with smart/intelligent product community, only a few works are interested in endowing products with capabilities that themmaintenance able to deal with disturbances related to maintenance engineering, and tomake influence decisions and strategiesand in anproblems autonomous, adaptive and resilient capabilities that that make themmaintenance able to to deal deal with disturbances disturbances and problems related to maintenance maintenance capabilities able with related to engineering, and tomake influence decisions inmaintenance anproblems autonomous, adaptive and resilient way. This position paperthem investigates literature to and shedstrategies light onand distribution possibilities. engineering, and to to influence influence maintenance decisions and strategies inmaintenance an autonomous, autonomous, adaptive possibilities. and resilient resilient engineering, and maintenance decisions and strategies in an adaptive and way. This position paper investigates literature to shed light on distribution Important questions are addressed, such as: What kinds of information and decisions are distributed in way. This position paper investigates literature to shed light on maintenance distribution possibilities. way. This position paper investigates literature to shed light on maintenance distribution possibilities. Important questions are addressed, such as: What kinds of information and decisions are distributed in maintenance engineering problems? How is such distribution achieved so far? What active roles do Important questions are addressed, such as: What kinds of information and decisions are distributed in Important questions are addressed, such as: What kinds of information and decisions are distributed in maintenance engineering problems? How is such distribution achieved so far? What active roles do smart/intelligent production objects, especially products, endorse in such architectures? What are the maintenance engineering problems? How is such distribution achieved so far? What active roles do maintenance engineering problems? How is such distribution achieved so far? What active roles do smart/intelligent production objects, especially products, endorse in such architectures? What are the benefits of such distribution, and how does it impact maintenance tasks in smart factories of the future? smart/intelligent production objects, especially products, endorse in such architectures? What are smart/intelligent production objects, especially products, endorse tasks in that such architectures? What are the the benefits of such and howand does it impact maintenance inare smart of the future? And finally, whatdistribution, are the challenges future research perspectives stillfactories worth investigating to benefits of such distribution, and how does it impact maintenance tasks in smart factories of the future? benefits of such distribution, and how does it impact maintenance tasks in smart factories of the future? And finally, what are the challenges and future research perspectives that are still worth investigating to foster developments in this field? Copyright © 2019 IFAC And finally, what are areinthe the challenges and future future research perspectives that that are are still still worth worth investigating investigating to to And what and research perspectives fosterfinally, developments thischallenges field? Copyright © 2019 IFAC Keywords: Distribution, Maintenance engineering, Classification, Smart/Intelligent foster developments in this field? Copyright © 2019 IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.Products,Architecture. All rights reserved. foster developments in this field? Copyright © 2019 IFAC Keywords: Distribution, Maintenance engineering, Classification, Smart/Intelligent Products,Architecture. Keywords: Maintenance engineering, engineering, Classification, Classification, Smart/Intelligent Smart/Intelligent Products,Architecture. Products,Architecture. Keywords: Distribution, Distribution, Maintenance 1. INTRODUCTION products with capabilities to make them able to deal with 1. INTRODUCTION products withandcapabilities to make them able to deal with disturbances problems related to maintenance engineering, 1. INTRODUCTION products with to them able to deal Recent years have a rapid development of 1. witnessed INTRODUCTION products withandcapabilities capabilities to make make themand able to deal inwith with disturbances problems related to maintenance engineering, and to influence maintenance decisions strategies an Recent years have witnessed a rapidSensor development of disturbances and problems related to maintenance engineering, innovative technologies, such as Wireless and Ad-hoc disturbances and problems related to maintenance engineering, and to influence maintenance decisions and strategies in an Recent years have witnessed a rapid development of autonomous, adaptive and resilient way (Andreadis, Bouzakis, Recent years have witnessed a rapidSensor development of innovative technologies, such as Wireless and Ad-hoc and to influence maintenance decisions and strategies in an Networks, Internet of Things (IoT), Cloud Computing, etc. and to influence maintenance decisions and strategies in an autonomous, adaptive and resilient way (Andreadis, Bouzakis, innovative technologies, such as Wireless Sensor and Ad-hoc et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et innovative technologies, such as Wireless Sensor and Networks, Internet Things (IoT), Cloud Computing, etc. autonomous, adaptive and resilient way (Andreadis, Bouzakis, (Zhong et al., 2017).ofThese technologies enabled newAd-hoc trends, autonomous, adaptive and resilient way (Andreadis, Bouzakis, et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et Networks, Internet of Things (IoT), Cloud Computing, etc. al., 2014). Although the rise of the Industry 4.0 paradigm is Networks, Internet of Things (IoT), Cloud Computing, etc. (Zhong al., 2017).(Wang, These technologies enabled et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et such as etubiquitous Ong and Nee, 2018)new andtrends, smart et al., 2014; Andreadis, Klazoglou, et al., 2014; Colledani et al., 2014). Although the rise of the Industry 4.0 paradigm is (Zhong etubiquitous al., 2017). 2017).(Wang, These technologies technologies enabled new trends, stimulating a burst of research (Lu, 2017), only a few (Zhong et al., These enabled new trends, such as Ong and Nee, 2018) and smart al., 2014). Although the rise of the Industry 4.0 paradigm is (Kusiak, 2018) manufacturing, and allowed developing new al., 2014). Although the rise of the Industry 4.0 paradigm is stimulating a burst of research (Lu, 2017), only a few such as as ubiquitous ubiquitous (Wang, Ong Ongand andallowed Nee, 2018) 2018) and smart smart researchers discuss the research impacts (Lu, of 2017), such technological such (Wang, and Nee, and (Kusiak, 2018) manufacturing, developing new stimulating a burst of only a few control architectures, based on multi-agent, holonic and stimulating a burst of research (Lu, 2017), only a few researchers discuss the impacts of such technological innovations on maintenance practices, architectures and (Kusiak, 2018) manufacturing, and allowed developing new (Kusiak,architectures, 2018) manufacturing, andmulti-agent, allowed developing control based on holonic new and researchers discuss the of technological bionic paradigms (Darmoul, Pierreval and Hajri–Gabouj, researchers discuss the impacts impacts of such such technological innovations on maintenance practices, architectures and control architectures, based multi-agent, holonic techniques. More generally, maintenance distribution choices control paradigms architectures, based on on multi-agent, holonic and and bionic (Darmoul, Pierreval and Hajri–Gabouj, innovations on maintenance practices, architectures and 2013). The main objective is to distribute the information innovations on maintenance practices, architectures and techniques. More generally, maintenance distribution choices bionic paradigms (Darmoul, Pierreval and Hajri–Gabouj, raise fundamental questions about maintenance organization, bionic The paradigms (Darmoul,is Pierreval andthe Hajri–Gabouj, 2013). main objective to distribute information techniques. More generally, maintenance distribution choices processing and to decentralize decision making over several techniques. More generally, maintenance distribution choices raise fundamental questions about maintenance organization, 2013). main objective information planning and operation that were not sufficientlyorganization, emphasized 2013). The Theand main objective is is to to distribute distribute the the over information processing to decentralize several raise questions about maintenance entities forand more flexibility, decision autonomymaking and resilience to raise fundamental fundamental questions aboutnot maintenance planning and operation that were sufficientlyorganization, emphasized processing to decentralize decision making over several and discussed in literature. processing and to decentralize decision making over several entities for more flexibility,and autonomy resilience to planning and operation that were not sufficiently emphasized change (Bayar, Hajri-Gabouj Darmoul,and 2018). planning and operation that were not sufficiently emphasized and discussed in literature. entities for more more flexibility,and autonomy and resilience to to entities for flexibility, autonomy resilience change (Bayar, Hajri-Gabouj Darmoul,and 2018). and discussed in literature. To fill in this gap, this position paper investigates literature to and discussed in literature. change Hajri-Gabouj Darmoul, 2018). Indeed, a growing research and effort focused on endowing change (Bayar, (Bayar, Hajri-Gabouj and Darmoul, 2018). To fill in this gap, this position paper investigatespossibilities, literature to shed light on maintenance distribution Indeed, a growing research effortrobots, focused on with endowing To fill in this gap, this position paper investigates literature production equipment (machines, etc.) some To fill in this gap, this position paper investigates literature to to shed light on maintenance distribution possibilities, Indeed, research effort focused on endowing opportunities and maintenance challenges. Important questions are Indeed, aa growing growing research effortrobots, focused on with endowing production equipment (machines, etc.) some shed light on distribution possibilities, kind of intelligence to carry production planning, control, and shed light on maintenance distribution possibilities, opportunities and challenges. Important questions are production equipment (machines, robots, etc.) with some addressed, such and as: What kinds of information and decisions are production equipment (machines, etc.) control, with some kind of intelligence to carry production and opportunities challenges. Important questions are maintenance engineering tasks (Giretrobots, etplanning, al., 2018). However, opportunities and challenges. Important and questions addressed, such as: What kinds of information decisions are kind of intelligence to carry production planning, control, and distributed in maintenance engineering problems? How is such kind of intelligence to carry production planning, control, and maintenance engineering (Giretfocused et al., 2018). However, addressed, such as: What kinds of information and decisions are only a limited number tasks of works on developing addressed, such as: What kinds of information and decisions are distributed in achieved maintenance problems? Howenabling is such maintenance engineering tasks (Giretfocused et al., al., 2018). 2018). However, distribution so engineering far in terms of both maintenance engineering tasks (Giret et However, only a limited number of works on developing distributed in maintenance engineering problems? How is such smart/intelligent product approaches, where products play an distributed in maintenance engineering problems? How is such distribution achieved so far in terms of both enabling only aa limited limited number of works works focused focused on developing developing technologies and controlsoarchitectures? What active roles do only of on smart/intelligent product approaches, where(Trentesaux products playand an distribution achieved far of both active role in number the decision processes distribution achieved far in in interms terms of active both enabling enabling technologies andproducts controlsoarchitectures? roles do smart/intelligent product approaches, where products play an smart/intelligent endorse suchWhat architectures? What smart/intelligent product approaches, where products play an active role in the decision processes (Trentesaux and technologies and control architectures? What active roles do Thomas, 2013; Aubry et al., 2016). Despite a growing technologies andof control architectures? What active roles do smart/intelligent products endorse in such architectures? What active role in the decision processes (Trentesaux and are the benefits such distribution, and how does it impact active role in the decision (Trentesaux and Thomas, 2013; Aubry et al., processes 2016). Despite a growing smart/intelligent products endorse in such architectures? What number of works on distributed maintenance (Upasani et al., smart/intelligent products endorse in such architectures? What are the benefits of such distribution, and how does it impact Thomas, et 2016). aa growing maintenance tasks in smart factories of the future? And finally, Thomas,of2013; 2013; Aubry et al., al., maintenance 2016). Despite Despite growing number works Aubry on distributed (Upasani al., are distribution, does it impact 2017), condition-based maintenance (CBM), andet on are the the benefits benefits of ofinsuch such distribution,ofand and how does it finally, impact maintenance smart the how future? And number of works on distributed maintenance (Upasani et al., what are the tasks challenges andfactories future research perspectives that number of works on distributed maintenance (Upasani 2017), condition-based maintenance (CBM), andet al., on maintenance tasks in smart factories of the future? And finally, Prognostics and Health Management (PHM) (Atamuradov et maintenance tasks in smart factories of the future? And finally, what are the challenges and future research perspectives that 2017), condition-based maintenance (CBM), and on are still worth investigating to foster developments in this field? 2017), condition-based maintenance (CBM), and on Prognostics and Health (Atamuradov et what are the challenges and future research perspectives that al., 2017), only a few Management works raise (PHM) the issue of endowing what are the challenges and future research perspectives that are still worth investigating to foster developments in this field? Prognostics and Health Health Management (PHM) (Atamuradov et Prognostics and (Atamuradov et al., 2017), only a few Management works raise (PHM) the issue of endowing are still worth investigating to foster developments in this field? are still worth investigating to foster developments in this field? al., only aa few works the of 2405-8963 © 2019, of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. al., 2017), 2017), only IFAC few(International works raise raiseFederation the issue issue of endowing endowing
Copyright 2019 responsibility IFAC 626Control. Peer review©under of International Federation of Automatic Copyright © 2019 IFAC 626 10.1016/j.ifacol.2019.11.089 Copyright 626 Copyright © © 2019 2019 IFAC IFAC 626
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Therefore, section 2 introduces maintenance organization and management in manufacturing systems. Section 3 introduces the main types of distribution architectures. Section 4 provides a classification of maintenance distribution possibilities. Section 5 positions the smart/intelligent product concept with respect to previously introduced control architectures and analyzes literature to bring answers to the previous research questions. Finally, section 6 discusses challenges and future research directions with respect to the industrial applications of smart/intelligent products in maintenance engineering. 2.
MAINTENANCE ORGANIZATION AND MANAGEMENT
Maintenance enables equipment, processes and engineering services to be available when required and ensures that the services retain their value as assets. Traditionally, maintenance activities have been carried out in a variety of ways by a variety of people. According to CIBSE (CIBSE, 2008), management of maintenance encompasses both ‘technical’ and ‘control’ aspects. The technical aspect includes determining what equipment is to be maintained, how and when; identifying problems and diagnosing causes; monitoring effects; preparing and analyzing records and technical information; initiating procedures to cope with situations before they arise; and ensuring that the chosen techniques are achieving the required results. The control aspect is aimed at minimizing the costs associated with the non-availability of an engineering service, and involves management of labor, spare parts and equipment to match the workload; locating where work is required; organizing transport; setting priorities; and coordinating action. It can extend to setting budgets, monitoring expenditure, identifying high maintenance cost plant and collecting information to form a basis for decision making. 3.
DISTRIBUTED ARCHITECTURES
Several classifications of distribution architectures were suggested and discussed in literature, with different levels of detail (Andreadis, Klazoglou, et al., 2014). The classification by Trentesaux (Trentesaux, 2009) is particularly interesting, mainly because of its conciseness and suitable level of abstraction. According to (Trentesaux, 2009), control architectures may be classified into centralized and noncentralized, this latter including fully hierarchical, fully heterarchical and semi heterarchical architectures (see Fig. 1). Control architecture
Centralized
Fully hierarchical
Noncentralized
Semi heterarchical Fully heterarchical
In centralized control, only one decision entity controls several resources (e.g. machines, robots, conveyors, etc.), activities (e.g. storage, processing, tool change, etc.), and/or processes (e.g. production, maintenance, quality inspection, etc.). For example, an industrial Programmable Logic Controller (PLC) is usually used to control a Flexible Manufacturing Cell (FMC), made of a machine, an automatic tool changer, a storage buffer, a conveyor and a robot for material handling. When the number of controlled entities is arbitrarily reduced, such as in this FMC example, this type of control leads to acceptable performance. However, a single controller at a factory level (e.g. a single PLC to control many FMCs) will face difficulties due to both realtime operational requirements (e.g. size and frequency of data streams, time lags between events and information processing, etc.), production system complexity (e.g. data acquisition, processing, communication, and storage, demand uncertainty, resource availability and reliability, etc.), and scalability (e.g. system upgrade, size improvements, new production capabilities, etc.). Decentralization of control is considered as a possible way to overcome these limitations. Two fundamental design choices guide the different ways to decentralize control decisions: using hierarchical or heterarchical relationships (Trentesaux, 2009). Hierarchical decentralization dates back to the early 1970s. Fully hierarchical systems are based exclusively on vertical, master-slave relationships between decision entities at different hierarchical control levels. Decision entities at lower hierarchical levels are closer to production resources and activities and provide information to decision entities at higher hierarchical levels. Based on this information, decision entities at higher hierarchical levels make decisions and control decision entities at lower hierarchical levels. Therefore, hierarchical decentralization consists in splitting the global control problem into hierarchically dependent sub-problems, each sub-problem being assigned to a decision level with decreasing time ranges (i.e., strategic, tactical and operational, such as planning, scheduling and supervision). This choice maintains sufficient long-term optimization, while supporting less short-term optimization (e.g., agility, reactivity). Although this approach has led to well-known and established information and production planning and control system architectures, such as the Manufacturing Resources Planning (MRP2) and the Enterprise Resource Planning (ERP), it is based on hard assumptions, such as the long-term availability and reliability of both supply and demand, the optimal behavior and high reliability of production resources, low product diversity, and the observability and controllability of all the possible internal variables (Trentesaux, 2009). In fully heterarchical control architectures, each decision entity can be seen as both a master and a slave, so that no hierarchy can be identified. Whereas hierarchy is a kind of “vertical” decentralization of control, heterarchy is a kind of “horizontal” decentralization of control. Decision entities have some degree of autonomy and work together to react quickly to disturbances instead of wasting response time lags to request control decisions from upper decision levels. Multi-agent systems is the
Fig. 1. Classification of distributed architectures. 627
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paradigm and technology usually used to implement fully heterarchical control (Shen et al., 2006; Leitão, 2009). In such control architectures, negotiation and cooperation appear as interaction processes in addition to coordination, which is the main (even unique) interaction mechanism in fully hierarchical architectures. However, negotiation and cooperation lead to new problems, for example, the need to prove deadlock avoidance mechanisms and more generally, the need to prove that sufficient level of performance can be attained (Trentesaux, 2009). The desire to integrate both hierarchical and heterarchical mechanisms into a distributed control system can be seen as an essential feature of semi heterarchical architectures, usually modelled and implemented through the holonic paradigm (Giret et al., 2018) in an attempt to benefit from the advantages of both approaches. 4.
DISTRIBUTED ARCHITECTURES FOR MAINTENANCE ENGINEERING
Although several researchers discussed the benefits of distributing maintenance activities and decision-making in manufacturing and service companies (Upasani et al., 2017), there is still a lack of classifications of maintenance distribution types and possibilities. We suggest such a classification in Fig. 2 to structure existing works on distributed maintenance. The classification shows four streams that capture the focuses of the current distributed architectures for maintenance, and that will be discussed in the following subsections.
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and reliability of the manufacturing facilities and engineering services and assets that contribute to adding value to manufactured products. In these references, several schemes were suggested to distribute maintenance information, activities and decision-making (see sections 4.2 to 4.4) on intelligent production objects (i.e. machines, products, etc.). On another hand, for products with high added value, high complexity (e.g. automobiles, airplanes, boats, submarines, satellites, etc.), which operate in harsh environments, and/or which are called to operate without human intervention (e.g. unmanned vehicles), a body of research focuses on developing self-monitoring, self-diagnosing, self-healing or self-repairing products, i.e. embedded systems that can perform some maintenance tasks on their own (Ghosh et al., 2007; McWilliam et al., 2018). However, it is worth noticing that, to the best of the authors’ knowledge, there is a lack of works that confer smart/intelligent products the capability to contribute to the maintenance of the system that manufactures them. 4.2. Functional distribution Standards from various organizations – such as the International Electro-technical Commission (IEC), the Institute of Electrical and Electronics Engineers (IEEE), the International Organization for Standardization (ISO), the Machinery Information Management Open Standards Alliance (MIMOSA), and the Society of Automotive Engineers (SAE) International – were suggested to structure maintenance functions, and to enable intelligent decision-making for improved performance, safety, reliability, and maintainability (Vogl, Weiss and Donmez, 2014). They cover standards for Condition Monitoring and Diagnostics (CM&D), Prognostics and Health Management (PHM), system development, data collection and analysis techniques, data management, system training, and software interoperability. Such standards guide and structure the design and development of functionally distributed maintenance approaches. For example, the OSACBM (Open System Architecture – Condition-Based Maintenance) standard (Sreenuch, Tsourdos and Jennions, 2013), released by MIMOSA, is dedicated to the development of conditional or provisional maintenance strategies, and can be adapted to each industrial need. It contains seven flexible modules, which content (methodology and algorithms) is configurable by the user. In their paper, Cachada et al. (Cachada et al., 2018) introduce the architecture of an intelligent, functionally distributed and predictive maintenance system, aligned with Industry 4.0 principles and following the structure of functional blocks of the OSA-CBM standard. 4.3. Geographical distribution
Fig. 2. Classification of maintenance distribution.
4.1. Distribution based on the targeted system This type of distribution appears as an answer to the question “what is to be maintained?”. Usually, researchers develop distributed maintenance approaches to ensure availability
With respect to the geographical position of the maintenance center relative to the production system, Karray et al. (Karray et al., 2009) discuss three possible distribution schemes: remote-maintenance, e-maintenance and s-maintenance. The remote-maintenance system consists of at least two computer systems that exchange data: a production management system that sends data to a maintenance management system, which 628
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analyzes transmitted data, and sends maintenance decisions back to the production management system. An emaintenance system is implemented on a cooperative distributed platform integrating different systems and maintenance applications of several production sites, such as in the European project PROTEUS (Bangemann et al., 2004). The s-maintenance architecture is intended to improve the performance of the e-maintenance architecture at the level of communication and data exchange between systems and allows considering data semantics and knowledge management (Matsokis et al., 2010). 4.4. Metaphor based distribution Metaphor based distribution stems from the fundamental idea that natural, biological, and social systems are able to perform complex tasks and to achieve robust and resilient performance while relying on simple and effective mechanisms. This idea provides instructive sources of inspiration for scientists to identify those architectures, concepts and mechanisms and develop adaptive systems that mimic them in order to be able to evolve and to deal with the new challenges facing manufacturing systems (Leitão, Barbosa and Trentesaux, 2012). Three paradigms are streaming search on metaphor based distribution: multiagent, holonic and bionic systems. These paradigms provide ways to identify control layers and decisional entities on which information will be distributed and decision making will be decentralized. A multi-agent system (MAS) is a computerized selforganized system composed of multiple interacting intelligent agents. Intelligence may include methodic, functional, procedural approaches, algorithmic search or machine learning. Several applications of MAS for distributed maintenance purposes are discussed in the literature. Gallab et al. (Gallab et al., 2017) provide a recent overview of existing research applying MAS to maintenance topics. The holonic concept originated from the work of Hungarian author and philosopher Arthur Koestler who tried to capture the behavior of complex systems by considering its constituent entities as being both wholes and parts at the same time (Babiceanu and Chen, 2006). Holonic control aims to merge the best properties of both hierarchical and heterarchical systems, namely, a high and predictable performance with a high robustness against disturbances and unforeseen changes. Holonic control is implemented using MAS. Therefore, advances in MAS theory and applications are beneficial to future development and enhancement of holonic systems. Giret et al. (Giret et al., 2018) provide a recent overview of holonic control applications in manufacturing systems. Biologically inspired engineering design uses analogies to biological systems to develop solutions for engineering problems. Leitão et al. (Leitão et al., 2012) provide an overview of some of the principles found in nature and biology and analyze their effectiveness to enhance multi-
agent systems to solve complex manufacturing engineering problems. However, they do not mention biological immunity as a trendy source of inspiration. Bayar et al. (Lee et al., 2011) fill in this gap and provide a review on fault detection, diagnosis and recovery using Artificial Immune Systems. Lee et al. (Lee et al., 2011) discuss research in the areas of distributed selfmaintenance for smart machines in manufacturing systems using Engineering Immune Systems (EIS). Fasanotti et al. (Fasanotti et al., 2016) describe a hybrid immune-MAS architecture, which aims to overcome the limitations of current predictive and preventive maintenance systems when they are applied to geographically widespread systems, such as in the cases of oil transfer systems via pipelines and wastewater treatment systems. 5.
PRODUCT INTELLIGENCE IN MAINTENANCE ENGINEERING
Several definitions and classifications of product intelligence were suggested and discussed in the literature, with different levels of detail (Meyer, Framling and Holmstrom, 2009; Främling et al., 2011). According to Porter and Heppelmann (Porter and Heppelmann, 2014), the capabilities of smart/intelligent products relative to maintenance engineering can be grouped into: monitoring, optimization, and autonomy. Monitoring refers to using sensors and external data sources to enable alerts and notifications of changes. Monitoring capabilities enable optimization of product operation and use in order to allow predictive diagnostics, service, and repair. Combining monitoring and optimization allows self-diagnosis and self-maintenance. Brahim-djelloul et al. (Brahim-djelloul et al., 2012) investigated the ability of products to trigger alerts following a break in the cold chain during transportation using RFID tags and temperature sensors integrated in the product packaging. The SURFER project (Mortellec et al., 2013) develops a holonic cooperative fault diagnosis approach, along with a generic architecture, to increase the embedded diagnosis capabilities of complex transportation systems. This concept is applied to the fault diagnosis of door systems of a railway transportation system. SURFER involves three types of holons: door holons, vehicle holons, and train holon. The diagnostic generated by the train holon is sent to the maintenance center, so that it can plan maintenance interventions. Lee et al. (Lee et al., 2014) suggest a Watchdog Agent embedded in a smart/intelligent product. Such an agent relies on sensors and computational prognostic algorithms to forecast future performance degradation and diagnose the reasons for degradation through trending and statistical modeling of the observed process signatures. With respect to self-maintenance systems, Takata et al. (Takata et al., 2004) identify three types of technologies: control, function redundancy, and network/group intelligence. A self-maintenance system is equipped with a control and reasoning processor, sensors, and actuators to perform these tasks. In case of failure, the smart/intelligent product can autonomously recognize the reasons for failure and try to reconfigure its state and its behavior to maintain its function (Urnes and Yeager, 1991). 629
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6.
DISCUSSION AND CONCLUSION
This paper suggested a classification of maintenance distribution possibilities and analyzed literature with respect to distributed maintenance architectures in general, and the role of active products in maintenance engineering in particular. The suggested classification is based on four streams that allow collecting the focuses of the current distributed architectures for maintenance. From this analysis, it appears that there is a lack of references of distributed maintenance architectures based on smart/intelligent products. To the best of the authors’ knowledge, there is no work that deals with smart/intelligent products contributing to maintain the system that manufactured them. To the question why such emergent distributed approaches are not adopted by industry, Leitão (Leitão, 2009) points out that, although the answer is not clear and unique, two groups of reasons can be identified: conceptual efficiency in the paradigms, and development-related aspects. Undoubtedly, the implementation of such approaches will be boosted by new ICT technologies, namely blockchain, advanced data analytics and augmented reality (Fernández et al., 2018). With the advent of Internet of Things, manufacturing systems and experts are witnessing a kind of transfer of responsibility from Man to intelligent objects. In traditional decision-making processes, it was the maintainer who used to reason, analyze and decide about the appropriate maintenance interventions. But with the advent of Internet of Things, smart products and smart objects in general are more and more able to self-monitor, self-diagnose, generate alerts and make decisions. The role of the maintainer is slowly moving from making maintenance decisions, to supervising, evaluating and selecting from sets of alternatives made by smart production objects. The main challenge is to design the distributed architecture that supports, enhances and enables the use of these technologies to the best of their potential and promises. REFERENCES Andreadis, G., Klazoglou, P., et al. (2014) ‘Classification and review of multi-agents systems in the manufacturing section’, Procedia Engineering. Elsevier B.V., 69, pp. 282– 290. doi: 10.1016/j.proeng.2014.02.233. Andreadis, G., Bouzakis, K., et al. (2014) ‘Review of AgentBased Systems in the Manufacturing Section’, Universal Journal of Mechanical Engineering, 2(2), pp. 55–59. Atamuradov, V. et al. (2017) ‘Prognostics and Health Management for Maintenance Practitioners - Review , Implementation and Tools Evaluation’, International Journal of Prognostics and Health Management, 8. Aubry, A. et al. (2016) ‘Product driven systems facing unexpected perturbations: How operational research models and approaches can be useful?’, in Studies in Computational Intelligence, 6th workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, SOHOMA 2016. Springer, pp. 259–267. doi: 10.1007/978-3-319-51100-9_23.
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