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Designing an Artificial Immune Systems for Intelligent Maintenance Systems
Proceedigs of the 15th IFAC Symposium on Proceedigs the 15th IFAC Symposium on Information of Control Problems in Manufacturing Available online at www.sciencedirect.com Information Control Problems in Manufacturing May 11-13, 2015. Ottawa, Canada Proceedigs of the 15th IFAC Symposium on May 11-13, 2015. Ottawa, Canada Information Control Problems in Manufacturing May 11-13, 2015. Ottawa, Canada
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Designing Designing an an Artificial Artificial Immune Immune Systems Systems for for Intelligent Intelligent Maintenance Maintenance Systems Systems Marcos Zuccolotto*,Carlos Eduardo Pereira*, Luca Fasanotti**, Sergio Cavalieri**,Jay Lee*** Designing an Artificial Immune Systems for Intelligent Maintenance Systems Marcos Zuccolotto*,Carlos Eduardo Pereira*, Luca Fasanotti**, Sergio Cavalieri**,Jay Lee*** Marcos Zuccolotto*,Carlos Eduardo Pereira*, Federal Luca Fasanotti**, Cavalieri**,Jay Lee*** *Electrical Engineering Department, University ofSergio Rio Grande do Sul *Electrical Engineering Department, Federal University of Rio Grande do Sul *Electrical Engineering Department, Federal University of Rio Grande do Sul Porto Alegre, Brasil( e-mail: [email protected], [email protected]). Porto Alegre, e-mail: [email protected], [email protected]). Porto Alegre, Brasil( Brasil( e-mail:Department, [email protected], [email protected]). *Electrical Engineering University of Rio Grande do Sul **CELS, Università Federal degli studi di Bergamo **CELS, Università degli studi di Bergamo **CELS, Università degli studi di Bergamo Porto Alegre, Brasil( e-mail: [email protected], [email protected]). Bergamo, Italy (e-mail : [email protected], [email protected] ) Bergamo, Italy Italy (e-mail (e-mail [email protected], [email protected] ) Bergamo, :: [email protected], **CELS, Università [email protected] Bergamo ***Intelligent Maintenance Systems degli (IMS)studi Center, University of Cincinnati) ***Intelligent Maintenance Systems (IMS) (IMS) Center, University of of Cincinnati Cincinnati) ***Intelligent Maintenance Systems University Bergamo, Italy Cincinnati, (e-mail : [email protected], [email protected] OH, USA( e-mailCenter, [email protected]). Cincinnati, OH, USA( (IMS) e-mailCenter, [email protected]). OH, USA( e-mail [email protected]). ***Intelligent Cincinnati, Maintenance Systems University of Cincinnati Cincinnati, OH, USA( e-mail [email protected]). Abstract: Engineering Immune Systems (EIS) are systems able to react against disturbances, to detect Abstract: Engineering Engineering Immune Immune Systems Systems (EIS) (EIS) are are systems systems able to to react react against against disturbances, disturbances, to detect Abstract: anomalous events and to adapt to environment changes in able order to keep a stable state. Autonomous anomalous events and to adapt to environment changes in order to keep aa stable state. Autonomous anomalous events and to adapt to environment changes in order to keep stable state. compose Autonomous Abstract: Engineering Immune Systems (EIS) are systems able to react against disturbances, to detect Computing and Artificial Immune Systems are biological inspired IT systems that could am Computing and Artificial Immune Systems are biological inspired IT systems that could compose am anomalous events and to adapt to environment changes in order to keep a stable state. Autonomous EIS. Artificial Immune Intelligent Maintenance System (AI2MS) is a system architecture proposal of a EIS. Artificial Intelligent (AI2MS) is IT a system architecture of a Computing andImmune Artificial Immune Maintenance Systems are System biological inspiredImmune systems that concepts. could proposal compose distributed Intelligent Maintenance System (IMS) using Artificial Systems AI2MSam is distributed Intelligent Maintenance System (IMS) using Artificial Immune Systems concepts. AI2MS is EIS. Artificial Immune Intelligent Maintenance Systemwhere (AI2MS) a system architecture proposal modeled and implemented using multi-agent systems, everyisautonomous IMS is mapped to aofseta modeled and implemented using multi-agent systems, everyImmune autonomous IMS is mapped to a set distributed Intelligent System (IMS) usingwhere Artificial Systems is of local agents, whileMaintenance the communication and decision process between IMSs areconcepts. mapped AI2MS to global of local agents, while the communication and decision process between IMSs are mapped to modeled and implemented using multi-agentlocally systems, autonomous IMSthat is mapped toglobal a set agents. Diagnose procedures are performed but,where if thisevery process find patterns have unknown agents. procedures are performed and locally but, ifprocess this process find IMSs patterns have unknown of localDiagnose agents, while the diagnose communication between arethat mapped global meaning, a collaborative process is decision started. This paper describes this immune toinspired meaning, a collaborative diagnose processlocally is started. This paper describes thisthat immune inspired agents. Diagnose procedures are performed but, if this process find patterns have unknown collaborative diagnostic strategy and its implementation by AI2MS agents. Preliminary results are collaborative diagnostic strategy and its implementation by agents. Preliminary results are collaborative diagnostic strategy andprocess its implementation by AI2MS AI2MS agents. results are meaning, collaborative diagnose is started.approach This paper describes this immune inspired presented, aderiving from the application of the proposed to a case study.Preliminary presented, deriving from the application of the proposed approach to a case study. presented, deriving from the application of the proposed approach to a case study. Preliminary results are collaborative diagnostic strategy and its implementation by AI2MS agents. Keywords: Fault detection, fault diagnosis, distributed artificial intelligent, intelligent maintenance © 2015, IFAC (International ofofAutomatic Control) Hosting Elsevier Ltd. All rightsmaintenance reserved. presented, deriving from theFederation application the proposed approach to by aintelligent, case study. Keywords: Fault detection, fault diagnosis, distributed artificial intelligent systems, artificial immune systems. systems, artificial systems. Keywords: Fault immune detection, fault diagnosis, distributed artificial intelligent, intelligent maintenance systems, artificial immune systems. 1. INTRODUCTION 1. INTRODUCTION INTRODUCTION 1. Condition Based Maintenance (CBM) is a management 1. INTRODUCTION Condition Based Maintenance (CBM) is a management philosophy that posits repair or replacement decision on the philosophy that posits repair or replacement on the Condition Based Maintenance (CBM) is decision a et management current or future condition of assets (Raheja al. 2006), current or future condition of assets (Raheja et al. on 2006), philosophy that posits repair or replacement decision the assessed by monitoring equipment through sensors that assessed byfuture monitoring equipment through sensors that assessed by monitoring equipment through sensors that current or condition of assets (Raheja et al. 2006), measure physical variables (e.g. vibration, sound, energy measure physical variables (e.g. vibration, sound, energy assessed by monitoring equipment sensors that consumption)associated with its through performance. This consumption)associated with its performance. This measure physical variables (e.g. vibration, sound, energy information can also be used to forecast the remaining useful information can also be used to forecast the remaining useful consumption)associated with its performance. This life (RUL) and optimize the maintenance schedule. Systems life (RUL) and optimize the maintenance Systems information can also be used to forecast theschedule. remaining that implement these prognostic characteristics areuseful also that implement these prognostic characteristics are also life (RUL) and optimize the maintenance schedule. Systems known as Intelligent Maintenance Systems (IMS) (Muller et known as Intelligent Maintenance (IMS) (Muller et that implement these prognostic Systems characteristics are also al. 2008). al. 2008). known as Intelligent Maintenance Systems (IMS) (Muller et The2008). use of CBM is a tendency in modern industry to improve al. The use of CBM is a tendency in modern industry to improve the production plant reliability, reducing breakdowns, their the production reliability,in reducing breakdowns, their The useon of maintenance CBMplant is a tendency moderncosts, industry improve impact and production and toincreasing impact on maintenance and production costs, and increasing the production plant reliability, reducing breakdowns, their production efficiency and safety(Jardine et al. 2006). production efficiency and safety(Jardine et al. 2006). impact on maintenance and production costs, and increasing Diagnosis and prognosis capabilities are pointed as important Diagnosis and prognosis and capabilities are pointed pointed asal.important important Diagnosis prognosis capabilities are production efficiency safety(Jardine et as 2006). features in and the servitization process, creating value by adding features in the servitization process, creating value by adding features in the servitization process, creating value by adding Diagnosis and prognosis capabilities are pointed as important services to products and migrating from a product centric to a services to the products and migrating a product centric to a features servitization process,from creating value by adding customerincentric approach (Holgado & Macchi 2014). customer centric approach (Holgado & Macchi 2014). services to products and migrating from a product centric to a Despite advances in IMS systems,&there are barriers for customer centric approach Macchi Despite advances in IMS(Holgado systems, there there are2014). barriers for for Despite systems, barriers predictingadvances reliabilityinofIMS a complex system as:are (a) the inability predicting reliability of a complex system as: (a) the inability predicting reliability a complex system (a)for the inability Despite advances inofIMS systems, thereas:are barriers for to anticipate unknown faults particularly complex to anticipate unknown faults particularly for complex predicting reliability of a complex system as: (a) the inability engineering systems; (b) the inability to sustain system engineering (b) faults the inability to sustain system to anticipatesystems; unknown for ofcomplex functionality and performance inparticularly the presence system functionality and performance in the presence of system engineering systems; (b) the inability to the sustain anomalies and severe disturbances; and (c) inability to anomalies andand severe disturbances; andpresence (c) the inability to functionality performance of system self-adjust system configurationsintothe mitigate internal faults self-adjust system configurations to and mitigate internal faults anomalies and severe disturbances; (c) the inability to and/or external intrusions. and/or external intrusions. self-adjust system configurations to mitigate internal faults and/or external intrusions.
Robustness, sustainability, resilience, and autonomy are all Robustness, sustainability, sustainability, resilience, resilience, and and autonomy autonomy are are all all Robustness, concepts that have been studied and used for designing concepts that have been studied and used for designing concepts thatsustainability, have been studied andand used for designing Robustness, resilience, autonomy areand all reliable engineering systems. Autonomic computing reliable engineering systems. Autonomic computing and concepts that have been studied and used for designing artificial immune systems are two examples of emerging artificial immune systems are Autonomic two examples of emerging reliable systems. and fields in engineering engineering that are making use ofcomputing those concepts fields in engineering that are making use of those concepts artificial immune them systems are two examples of emerging and implementing to achieve higher reliability. and implementing them to achieve higher fields in engineering that are making usereliability. of those concepts The implementing concept of them an engineering immune system (EIS) and to achieve higher reliability. The concept of an engineering immune system (EIS) introduced by Lee in (Lee et al. 2011) describes a system that introduced by Lee (Lee et al. 2011)immune describessystem a system(EIS) that The concept of in an engineering can use autonomic control to react to any disturbances and can use autonomic control to react to any disturbances and introduced by Lee back in (Lee 2011) describes a system that return the system to et a al. stable state. This would require return system back This would require can usetheautonomic control tostable react to any disturbances and the immune system to to beaable to state. first identify anomalous the immune system to to beaable to state. first identify anomalous return the system back stable This would require events and devise the appropriate action plan to return the events and devise thetoappropriate plan to anomalous return the the immune system beMoreover, able to action first identify system back to stability. the EIS will have to be system backdevise to stability. stability. Moreover,action the EIS EIS will have to the be system back to Moreover, the will to be events the appropriate plan to have return adaptiveand in order to be resilient, to reach new states of adaptive in order to be resilient, to reach new states of adaptive in inorder to be resilient, to reach newhave states of system back toresponse stability. thechanges. EIS will to be equilibrium toMoreover, environment equilibrium response to environment adaptive in inorder to be resilient, to changes. reach new states of Conceive an EIS is a huge challenge due to its complexity equilibrium in response to environment changes. Conceive an EIS a hugetechnology. challenge due its complexity and limitation of is current To to address some of and limitation limitation of is current technology. To to address some of of and of current technology. To address some Conceive an EIS a huge challenge due its complexity these challenges, an architecture for a distributed IMS using these challenges, an architecture for a distributed IMS using these challenges, architecture a distributed IMS using and limitation of an current technology. To address some of Artificial Immune Systems (AIS)forconcepts, called AI2MS, Artificial ImmuneanSystems (AIS)forconcepts, called AI2MS, these challenges, architecture a distributed IMS using was proposed by Zuccolotto (Zuccolotto et al. 2013). The was proposed by Zuccolotto (Zuccolotto et called al. 2013). The Artificial Immune Systems concepts, AI2MS, AI2MS was conceived as a (AIS) multi-agent system, in order to AI2MS was conceived as a multi-agent system, in order to was proposed by Zuccolotto et al. from 2013). The get the distributed, adaptive and(Zuccolotto scalable features AIS. get the distributed, adaptive scalable features AIS. to AI2MS was conceived as aand multi-agent system,from in order Thisthe work presentsadaptive new steps inscalable AI2MSfeatures development, with get distributed, and from AIS. This work presents new steps in AI2MS development, with focus on the collaborative diagnostic adopted in order to use focus on thepresents collaborative diagnostic adopted in order towith use This work new in AI2MS development, information provided bysteps machines in production plant to information provided by machines in production plant to focus onanomalous the collaborative identify events. diagnostic adopted in order to use identify anomalous events. information provided by machines in production plant to This paper is organized as follow: Section 2 presents identify anomalous events. as This paper is organized follow: Section 2 presents concepts about artificial immune algorithms and application, concepts about artificial immune algorithms This is organized as section follow: 2thepresents sectionpaper 3 describes the AI2MS, 4 Section dealand withapplication, role of section 3 describes the AI2MS, section 4 deal with the role of concepts about artificial immune algorithms and application, section by 3 describes theAll AI2MS, section 4 deal with the role of 2405-8963 © 2015, IFAC (International Federation of Automatic Control) Hosting Elsevier Ltd. rights reserved.
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a Collaborative agent and its implementation and section 5 describes a case study. Preliminary results are presented on section 6 and in section 7 conclusion are drawn and future work directions are signalled. 2.
ENGINEERING IMMUNE SYSTEMS (EIS)
The main idea of EIS is to design highly reliable systems that are capable of surviving any disruptions without serious failures, with the capability to resist disturbances while maintaining their stability. EIS is considered an intelligent system that has the ability to assess and predict system performance and health based on available data using different modeling techniques. The focus of engineering immune systems is on mechanical engineering systems and on making use of prognostics and health management techniques in order to reach a mechanical system that has self-immunity (Lee et al. 2011). The idea of EIS was inspired by both biological immune system and the human nervous system and two research fields that could found EIS development are autonomic computing and artificial immune systems. Autonomic computing is defined as computing systems that are capable of managing themselves and adjusting to varying circumstances with minimal human intervention. This vision was initiated by IBM and the idea is analogous to the human autonomic nervous system that is responsible for vital functions in the human body without any conscious recognition or interference from the human being (Kephart & Chess 2003) (Kephart 2005). The autonomic computing paradigm should have some sort of mechanism to controls the state of the system, such that any disruptive change in one of its essential variables would trigger an action in the computing system that brings it back to equilibrium internally and with its environment (Parashar & Hariri 2005).For an autonomic computing system to attain self-management, the system must have four properties: self-configuring, selfhealing, self-optimization and self-protection.
anomalous events or complex self-configurations could be done by cloud-computing. 3.
ARTIFICIAL IMMUNE MAINTENANCE SYSTEM
INTELLIGENT
Artificial Immune Intelligent Maintenance System (AI2MS) is an adaptive, distributed, multi-level IMS proposed to explore the advantages of the distributed nature, pattern recognition and learning capabilities of the AIS. Conceived as a multi-agent system, AI2MS could dynamically change the features of each system component, changing the number of agents, the agent behaviours or introducing new agents. These features allow the development of self-configuration and self-optimization capabilities. AI2MS is composed by several different agents, each providing a specific functionality. Agents were organized in 3 layers: Device, Collaborative and Plant Layers. 3.1 Device Layer Agents Device Layer is the primary layer, provides features of fault detection and device health assessment on a single device. Fig. 1 depicts the agents of this layer and its basic relationships. Device Health Assessment Agent (DHAA) is responsible for the health evaluation of the device and also manages local resources. It can request creation of (New) Fault Detector Agents, Collaborative Agents and requesting maintenance services when needed. Each device has one DHAA, which interacts with all agents in the device and with the Plant Health Assessment Agent, responsible to manage the overall plant maintenance.
The immune system is a remarkable information processing and self-learning system that offers inspiration to build artificial immune system (AIS), that tries to reproduce their strategies to acquire features as self-organisation, learning, memory, adaptation, recognition, robustness and scalability (Dasgupta & Forrest 1999) (Dasgupta et al. 2011). Basic function of immune systems is to recognize cells that are not natural from body, called non-self cells or antigens, and eliminate them. This task is performed by a complex network of specialized cells called lymphocytes. To search for non-self elements lymphocytes produces molecules called antibodies. When and antibody express affinity with an antigen, immune reaction is trigged. Theories that explain this reaction brought inspiration for AIS algorithms (Liu et al. 2006). Following the biological analogy, EIS could be conceived as a multi-layer system, where the ordinary diagnostic/fault detection could be performed locally by adaptive embedded system and task as plan and execute action to mitigate
Fig. 1 - Device layer agents use case Sensors Agents (SA): A SA is linked to a physical sensor that monitors one or more parts of a device and is a source of raw or pre-processed data for other agents.
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Fault Detection Agents (FDA) are responsible for detection specific failure and diagnose of specific parts. A fault pattern detected by FDA is reported to DHAA. New Fault Detection Agents (NFDA) are agents that looking for the presence of new “antigens”, patters which meaning are unknown by the system.
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AI2MS IMPLEMENTATION
The implementation of AI2MS is being carried out using JADE framework, a FIPA compliant MAS platform (Bellifemine et al. 2008). Details about agent model methodology, message exchange and ontology are described in previous work (Zuccolotto, Regal, et al. 2014).
3.2 Collaborative Layer Agents
4.1 Antigens, affinity function and antibodies creation
Agents on collaborative layer are responsible to promote knowledge exchange between devices, managing the training of (N) FDA using shared data set and the classification of new “antibodies”. Fig. 2 Error! Reference source not found.shows the relationships between these agents with DHHA and the maintenance experts. Evolution Agents (EA) are the agent that keeps knowledge about the feature space, e.g., is a specified pattern represents a “antigen” or it is a self-pattern, and this space is partially unknown. EA can also evaluate antibodies in order to define how close they are to known antigens.
Sensor agent produce data associated to one or more physical parts of the equipment. In order to reduce messages size and avoid communication overload, an embedded feature extraction is performed by the SA. To reinforce AIS approach, this data is called Antigen although a better definition should be Antigen Candidate and, in the same way, detectors designed to identify Antigens are called Antibodies. Fig. 3 depicts the characteristic of each one of these elements using a class diagram.
Fig. 3. - Antigen and antibody structures Fig. 2 - Collaborative layer use case Collaborative Agents (CA) are responsible of classifying the unknown antibodies, e.g., antibodies belonging to NFDA that find an antigen. This is performed using different techniques: send the antibody found to other similar devices, collecting data about detected antibodies these devices, evaluating the current classification of the antibody with help of the EA. Result of this classification is presented to an expert and incorporated in to the system knowledge.
Feature class presented in Fig. 3 define the data format, Affinity class implements affinity function. Antibody class keeps the information of category (Know, Unknown, Mutated) and detection flag. Mutation method is used to generate new antibodies derived from specific one. Affinity is measured by the Euclidian distance and the antibody generation, performed by Evolution Agent, is a modification of Variable Radius Negative Selection Algorithm (VRNSA), described in (Zuccolotto, Fasanotti, et al. 2014).
3.3 Plant Layer Agents 4.2 Exploring featuring space Agents on plant layer are responsible overall management of the system. Main component of this layer is the Plant Health Assessment Agent (PHAA) that is responsible to estimate the health conditions of the entire plant. The Failure Mode Update Agent acts as a vaccine, to support equipment installed in poor network condition areas and the Update Training Agent collects sensor data and transfer them to a database, improving training data sets and allowing big data applications.
Evolution agent uses a previously acquired dataset to build the feature space that represents the knowledge of about equipment fault modes as well as about the normal behaviour. To improve this knowledge during operation, feature space exploration is performed by NewFaultMode Agents, using a strategy based on clonal selection algorithms. NFDA agents (and its antibodies) are created by antibody mutation or by a fully random process. NFDA has a limited time life that is increased at each hit detection.
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Creation of NFDA derived from FDA detection is presented on Fig.4. The antigen detected by FDA is reported to DHAA that reports to Plant Agent and requests to Evolution Agent the creation of a New Fault detection derived from the antigen detected. Main goal of this approach is to explore the feature space regarding the identified fault mode found.
Fig. 6 - Collaborative search Detection of these distributed antibodies starts the categorization process presented on Fig. 7. Each antibody detected is classified against each fault/normal mode by Evolution Agent. Possible new fault mode is also evaluated by Collaborative Agent using classification algorithms through a method called CollaborativeClassification.
Fig.4 - FDA detection and NFDA creation When a NewFaultDetection agent hit an antigen, a collaborative process is fired. Other devices from the same type are searched and in case some are found, a comparison of the antibodies is performed. Fig. 5 depicts this process.
Fig. 7 - Categorization and incorporation process Fig. 5 - NFDA detection After receiving a report from NFDA, DHAA creates a new NFDA (depending on resource availability) and requests a search process to Collaborative Agent. 4.3 Collaborative diagnostic Collaborative search is the first stage of collaborative diagnostic. The antibody, found in one device, is propagated to similar devices in the network by the collaborative agent, as shown in Fig. 6. Besides the request for agent creation, Collaborative agent also collects detection's reports from DHAA, used to classify the new antibodies.
Final evaluation is reported to maintenance expert, who have the ultimate decision. Final results are added to knowledge base by the Evolution Agent. The first metric chosen to implement the Collaborative classification method is the Mahalanobis distance (Xiang et al. 2008). Mahalanobis distance accounts for unequal variances as well as correlations between features and could adequately evaluate the distance by assigning different weights or importance factors to the features of data points, improving performance of clustering algorithms In order to classify an antigen, the index NMd, described in (1) was used.
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𝑁𝑁𝑁𝑁𝑁𝑁(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔) =
𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎, ̅̅̅̅̅̅̅̅) 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔)
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represented by red circles, also marking the closest border points or each group.
(1)
̅̅̅̅̅̅̅̅) is the Mahalanobis distance Where 𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 between antigen and the mean value of features of a group (Normal, Fault-n) and ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔)is the mean Mahalanobis distance of each group component.
Table 1 to Table 3 presents the system detection report for each scenario in a format called confusion matrix. Confusion Matrix shows in the diagonal the positive detection hits and negative detection on the other cells.
A threshold value is used to select a target antigen as belonging to a group and is defined as the maximum NMd (antig, group) calculated over all group samples. Combined action of NFDA, exploring the feature space, and the CA, spreading NFDA trough the equipment network and classifying these new “antibodies” adapts the AI2MS detector set to changes in operation and environmental conditions. Incorporating this information on the health assessment algorithm (performed by DHAA) and using this metric to modify equipment operation configuration (to extend the RUL) or reschedule maintenance (to restore equipment’s health) could give resilience capabilities to the production system. 5.
CASE STUDY
As a case study for this work is an electric valve actuator model CS06 from the Brazilian company Coester was selected. Test bench and data acquisition systems developed for this products are presented in (Zuccolotto, Fasanotti, et al. 2014).
Fig. 8 – Coester actuator feature space Table 1 – Detection report for Test Set A
Data acquired was classified in three groups, Normal (75 cycles samples), Fault 1(gear wear - 50 cycles samples) and Fault 2(gear broken 25 cycles samples). As feature selection algorithm, Wavelet Packet Energy (WPE) with Mother-wavelet Daubechies 6 was implemented due to the satisfactory results achieved in previous works (Gonçalves et al. 2011) and (Piccoli et al. 2012). The WPE coefficients 1 and 6 were selected as significant features. Initial tests were run to evaluate the performance of fault detectors and collaborative classification. First experimental setup was done using one computer, on which three JADE containers were created, each running one device layer. Evolution agents and Collaborative agents are running on main container. Sensor agents get data directly from files. Test sets were created through random choice of each group, 10 cycles from Normal, 10 cycles from Fault1 and 5 cycles from Fault2. Training sets were generated with fixed length of 20 cycles, in three scenarios: A: Normal + Fault1 + Fault2; B: Normal + Fault1; C: Normal + Fault2. This organization allows the use of Fault2 on scenario B and Fault1 on scenario C as unknown antigens, to test the collaborative diagnostic.
C l a s
Feature space is presented in Fig. 8. Normal behaviour features are represented by blue dots, Fault 1 by green and fault 2 by brow dots. Space covered by each detector is
Total Normal Fault1 Fault2 Unknown Accuracy : 99,3
Fault1 60
Fault2 30
59 (98,3%) 30 1 (1,7 %)
Table 2 – Detection report for Test Set B
C l a s
Test Set B Normal 60 59 (98,3%) 1 (1,7%)
Total Normal Fault1 NewGroup Unknown Accuracy : 98,7 %
Fault1 60
Fault2 30
60 29(96,7%) 1 (3,3%)
Table 3 – Detection report for Test Set C
6. RESULTS Six Test set were generated for each scenario, with a total amount of 150 signals. Each device runs 2 Test sets.
Test Set A Normal 60 60
C l a s
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Test Set C Normal Fault1 Total 60 60 Normal 60 NewGroup 32 (53,3%) Fault2 25 (41,7%) Unknown 3 Accuracy : 81,3%
Fault2 30
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7. CONCLUSIONS This paper deals with the concept of Engineering Immune Systems and describes the preliminary results in one stage of the development of the distributed IMS based on Artificial Immune Systems (AIS) that could compose an EIS. A collaborative procedure was proposed to classify detected patterns which categorization was previously unknown. Current artificial immune algorithms explore the idea of trying different detectors on the same training set. Collaborative diagnose applies the same detector with different signal sources in real-time operation. A possible analogy is the physician that identifies the same symptom is different patients, leading to the same root cause. This work evaluates the accuracy of proposed methodology and metrics adopted. The overall accuracy was 93,1% , similar to other AIS proposals (Laurentys et al. 2010), pointing to a promising solution. Worst result was obtained in scenario C, where Fault1 was absent. In this specific case the detectors size, defined by the distance between clusters, is larger than the other cases, increasing the number of misclassification. In this case, Fault2 detectors hits features related to Fault1. The following activities will be carried out the further development of our proposal:
Analysis of the intensity and quality of the message exchange will be included, in order to evaluate the requirements to the network support and the possibility of integration with the plant control network.
Implementation of dynamic change of agent methods, to allow dynamic system reconfiguration.
Adaptive Clustering as Collaborative Classification Method will be evaluated.
ACKNOWLEDGMENT This work is part of a collaborative research activity between UFRGS and University of Bergamo within the ProSSaLiC Project, funded by European Community’s FP7/2007-2013 under grant agreement n° PIRSES-GA-2010-269322. This work is also supported by CAPES Brazilian Research Agency under contract 99999.008167/2014-01. REFERENCES Bellifemine, F. et al., 2008. JADE: A software framework for developing multi-agent applications. Lessons learned. Information and Software Technology, 50(1-2), pp.10– 21. Dasgupta, D. & Forrest, S., 1999. Artificial immune systems in industrial applications. In Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM’99. IEEE, pp. 257– 267 vol.1.
Dasgupta, D., Yu, S. & Nino, F., 2011. Recent Advances in Artificial Immune Systems: Models and Applications. Applied Soft Computing, 11(2), pp.1574–1587. Holgado, M. & Macchi, M., 2014. Exploring the role of Emaintenance for value creation in service provision. 2014 International Conference on Engineering, Technology and Innovation (ICE), pp.1–10. Jardine, A.K.S., Lin, D. & Banjevic, D., 2006. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), pp.1483–1510. Kephart, J. & Chess, D., 2003. The vision of autonomic computing. Computer, pp.41–50. Kephart, J.O., 2005. Research challenges of autonomic computing. In Proceedings of 27th International Conference on Software Engineering. St. Louis: IEEe, pp. 15–22. Laurentys, C. a. et al., 2010. Design of an Artificial Immune System for fault detection: A Negative Selection Approach. Expert Systems with Applications, 37(7), pp.5507–5513. Lee, J., Ghaffari, M. & Elmeligy, S., 2011. Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems. Annual Reviews in Control, 35(1), pp.111–122. Liu, F., Wang, Q. & Gao, X., 2006. Survey of artificial immune system. International Symposium on Systems and Control in Aerospace and Astronautics, pp.985–989. Muller, A., Crespo Marquez, A. & Iung, B., 2008. On the concept of e-maintenance: Review and current research. Reliability Engineering & System Safety, 93(8), pp.1165–1187. Parashar, M. & Hariri, S., 2005. Autonomic computing: An overview. Unconventional Programming Paradigms, pp.257–269. Raheja, D. et al., 2006. Data fusion/data mining-based architecture for condition-based maintenance. International Journal of Production Research, 44(14), pp.2869–2887. Xiang, S., Nie, F. & Zhang, C., 2008. Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition, 41(12), pp.3600– 3612. Zuccolotto, M. et al., 2013. A Distributed Intelligent Maintenance Approach based on Artificial Immune Systems. In World Congress on Engineering Asset Management. pp. 1–14. Zuccolotto, M., Fasanotti, L., et al., 2014. Artificial Immune Intelligent Maintenance System – Diagnostic Agents. In E. Boje & X. Xia, eds. Proceedings of the 19th IFAC World Congress. Cape Town: International Federation of Automatic Control, pp. 7116–7121. Zuccolotto, M., Regal, T., et al., 2014. Device Layer Agents for an Artificial Immune Intelligent Maintenance System. In IEEE International Conference on Industrial Informatics 2014. Porto Alegre: IEEE, pp. 554 – 559.
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Designing Designing an an Artificial Artificial Immune Immune Systems Systems for for Intelligent Intelligent Maintenance Maintenance Systems Systems Marcos Zuccolotto*,Carlos Eduardo Pereira*, Luca Fasanotti**, Sergio Cavalieri**,Jay Lee*** Designing an Artificial Immune Systems for Intelligent Maintenance Systems Marcos Zuccolotto*,Carlos Eduardo Pereira*, Luca Fasanotti**, Sergio Cavalieri**,Jay Lee*** Marcos Zuccolotto*,Carlos Eduardo Pereira*, Federal Luca Fasanotti**, Cavalieri**,Jay Lee*** *Electrical Engineering Department, University ofSergio Rio Grande do Sul *Electrical Engineering Department, Federal University of Rio Grande do Sul *Electrical Engineering Department, Federal University of Rio Grande do Sul Porto Alegre, Brasil( e-mail: [email protected], [email protected]). Porto Alegre, e-mail: [email protected], [email protected]). Porto Alegre, Brasil( Brasil( e-mail:Department, [email protected], [email protected]). *Electrical Engineering University of Rio Grande do Sul **CELS, Università Federal degli studi di Bergamo **CELS, Università degli studi di Bergamo **CELS, Università degli studi di Bergamo Porto Alegre, Brasil( e-mail: [email protected], [email protected]). Bergamo, Italy (e-mail : [email protected], [email protected] ) Bergamo, Italy Italy (e-mail (e-mail [email protected], [email protected] ) Bergamo, :: [email protected], **CELS, Università [email protected] Bergamo ***Intelligent Maintenance Systems degli (IMS)studi Center, University of Cincinnati) ***Intelligent Maintenance Systems (IMS) (IMS) Center, University of of Cincinnati Cincinnati) ***Intelligent Maintenance Systems University Bergamo, Italy Cincinnati, (e-mail : [email protected], [email protected] OH, USA( e-mailCenter, [email protected]). Cincinnati, OH, USA( (IMS) e-mailCenter, [email protected]). OH, USA( e-mail [email protected]). ***Intelligent Cincinnati, Maintenance Systems University of Cincinnati Cincinnati, OH, USA( e-mail [email protected]). Abstract: Engineering Immune Systems (EIS) are systems able to react against disturbances, to detect Abstract: Engineering Engineering Immune Immune Systems Systems (EIS) (EIS) are are systems systems able to to react react against against disturbances, disturbances, to detect Abstract: anomalous events and to adapt to environment changes in able order to keep a stable state. Autonomous anomalous events and to adapt to environment changes in order to keep aa stable state. Autonomous anomalous events and to adapt to environment changes in order to keep stable state. compose Autonomous Abstract: Engineering Immune Systems (EIS) are systems able to react against disturbances, to detect Computing and Artificial Immune Systems are biological inspired IT systems that could am Computing and Artificial Immune Systems are biological inspired IT systems that could compose am anomalous events and to adapt to environment changes in order to keep a stable state. Autonomous EIS. Artificial Immune Intelligent Maintenance System (AI2MS) is a system architecture proposal of a EIS. Artificial Intelligent (AI2MS) is IT a system architecture of a Computing andImmune Artificial Immune Maintenance Systems are System biological inspiredImmune systems that concepts. could proposal compose distributed Intelligent Maintenance System (IMS) using Artificial Systems AI2MSam is distributed Intelligent Maintenance System (IMS) using Artificial Immune Systems concepts. AI2MS is EIS. Artificial Immune Intelligent Maintenance Systemwhere (AI2MS) a system architecture proposal modeled and implemented using multi-agent systems, everyisautonomous IMS is mapped to aofseta modeled and implemented using multi-agent systems, everyImmune autonomous IMS is mapped to a set distributed Intelligent System (IMS) usingwhere Artificial Systems is of local agents, whileMaintenance the communication and decision process between IMSs areconcepts. mapped AI2MS to global of local agents, while the communication and decision process between IMSs are mapped to modeled and implemented using multi-agentlocally systems, autonomous IMSthat is mapped toglobal a set agents. Diagnose procedures are performed but,where if thisevery process find patterns have unknown agents. procedures are performed and locally but, ifprocess this process find IMSs patterns have unknown of localDiagnose agents, while the diagnose communication between arethat mapped global meaning, a collaborative process is decision started. This paper describes this immune toinspired meaning, a collaborative diagnose processlocally is started. This paper describes thisthat immune inspired agents. Diagnose procedures are performed but, if this process find patterns have unknown collaborative diagnostic strategy and its implementation by AI2MS agents. Preliminary results are collaborative diagnostic strategy and its implementation by agents. Preliminary results are collaborative diagnostic strategy andprocess its implementation by AI2MS AI2MS agents. results are meaning, collaborative diagnose is started.approach This paper describes this immune inspired presented, aderiving from the application of the proposed to a case study.Preliminary presented, deriving from the application of the proposed approach to a case study. presented, deriving from the application of the proposed approach to a case study. Preliminary results are collaborative diagnostic strategy and its implementation by AI2MS agents. Keywords: Fault detection, fault diagnosis, distributed artificial intelligent, intelligent maintenance © 2015, IFAC (International ofofAutomatic Control) Hosting Elsevier Ltd. All rightsmaintenance reserved. presented, deriving from theFederation application the proposed approach to by aintelligent, case study. Keywords: Fault detection, fault diagnosis, distributed artificial intelligent systems, artificial immune systems. systems, artificial systems. Keywords: Fault immune detection, fault diagnosis, distributed artificial intelligent, intelligent maintenance systems, artificial immune systems. 1. INTRODUCTION 1. INTRODUCTION INTRODUCTION 1. Condition Based Maintenance (CBM) is a management 1. INTRODUCTION Condition Based Maintenance (CBM) is a management philosophy that posits repair or replacement decision on the philosophy that posits repair or replacement on the Condition Based Maintenance (CBM) is decision a et management current or future condition of assets (Raheja al. 2006), current or future condition of assets (Raheja et al. on 2006), philosophy that posits repair or replacement decision the assessed by monitoring equipment through sensors that assessed byfuture monitoring equipment through sensors that assessed by monitoring equipment through sensors that current or condition of assets (Raheja et al. 2006), measure physical variables (e.g. vibration, sound, energy measure physical variables (e.g. vibration, sound, energy assessed by monitoring equipment sensors that consumption)associated with its through performance. This consumption)associated with its performance. This measure physical variables (e.g. vibration, sound, energy information can also be used to forecast the remaining useful information can also be used to forecast the remaining useful consumption)associated with its performance. This life (RUL) and optimize the maintenance schedule. Systems life (RUL) and optimize the maintenance Systems information can also be used to forecast theschedule. remaining that implement these prognostic characteristics areuseful also that implement these prognostic characteristics are also life (RUL) and optimize the maintenance schedule. Systems known as Intelligent Maintenance Systems (IMS) (Muller et known as Intelligent Maintenance (IMS) (Muller et that implement these prognostic Systems characteristics are also al. 2008). al. 2008). known as Intelligent Maintenance Systems (IMS) (Muller et The2008). use of CBM is a tendency in modern industry to improve al. The use of CBM is a tendency in modern industry to improve the production plant reliability, reducing breakdowns, their the production reliability,in reducing breakdowns, their The useon of maintenance CBMplant is a tendency moderncosts, industry improve impact and production and toincreasing impact on maintenance and production costs, and increasing the production plant reliability, reducing breakdowns, their production efficiency and safety(Jardine et al. 2006). production efficiency and safety(Jardine et al. 2006). impact on maintenance and production costs, and increasing Diagnosis and prognosis capabilities are pointed as important Diagnosis and prognosis and capabilities are pointed pointed asal.important important Diagnosis prognosis capabilities are production efficiency safety(Jardine et as 2006). features in and the servitization process, creating value by adding features in the servitization process, creating value by adding features in the servitization process, creating value by adding Diagnosis and prognosis capabilities are pointed as important services to products and migrating from a product centric to a services to the products and migrating a product centric to a features servitization process,from creating value by adding customerincentric approach (Holgado & Macchi 2014). customer centric approach (Holgado & Macchi 2014). services to products and migrating from a product centric to a Despite advances in IMS systems,&there are barriers for customer centric approach Macchi Despite advances in IMS(Holgado systems, there there are2014). barriers for for Despite systems, barriers predictingadvances reliabilityinofIMS a complex system as:are (a) the inability predicting reliability of a complex system as: (a) the inability predicting reliability a complex system (a)for the inability Despite advances inofIMS systems, thereas:are barriers for to anticipate unknown faults particularly complex to anticipate unknown faults particularly for complex predicting reliability of a complex system as: (a) the inability engineering systems; (b) the inability to sustain system engineering (b) faults the inability to sustain system to anticipatesystems; unknown for ofcomplex functionality and performance inparticularly the presence system functionality and performance in the presence of system engineering systems; (b) the inability to the sustain anomalies and severe disturbances; and (c) inability to anomalies andand severe disturbances; andpresence (c) the inability to functionality performance of system self-adjust system configurationsintothe mitigate internal faults self-adjust system configurations to and mitigate internal faults anomalies and severe disturbances; (c) the inability to and/or external intrusions. and/or external intrusions. self-adjust system configurations to mitigate internal faults and/or external intrusions.
Robustness, sustainability, resilience, and autonomy are all Robustness, sustainability, sustainability, resilience, resilience, and and autonomy autonomy are are all all Robustness, concepts that have been studied and used for designing concepts that have been studied and used for designing concepts thatsustainability, have been studied andand used for designing Robustness, resilience, autonomy areand all reliable engineering systems. Autonomic computing reliable engineering systems. Autonomic computing and concepts that have been studied and used for designing artificial immune systems are two examples of emerging artificial immune systems are Autonomic two examples of emerging reliable systems. and fields in engineering engineering that are making use ofcomputing those concepts fields in engineering that are making use of those concepts artificial immune them systems are two examples of emerging and implementing to achieve higher reliability. and implementing them to achieve higher fields in engineering that are making usereliability. of those concepts The implementing concept of them an engineering immune system (EIS) and to achieve higher reliability. The concept of an engineering immune system (EIS) introduced by Lee in (Lee et al. 2011) describes a system that introduced by Lee (Lee et al. 2011)immune describessystem a system(EIS) that The concept of in an engineering can use autonomic control to react to any disturbances and can use autonomic control to react to any disturbances and introduced by Lee back in (Lee 2011) describes a system that return the system to et a al. stable state. This would require return system back This would require can usetheautonomic control tostable react to any disturbances and the immune system to to beaable to state. first identify anomalous the immune system to to beaable to state. first identify anomalous return the system back stable This would require events and devise the appropriate action plan to return the events and devise thetoappropriate plan to anomalous return the the immune system beMoreover, able to action first identify system back to stability. the EIS will have to be system backdevise to stability. stability. Moreover,action the EIS EIS will have to the be system back to Moreover, the will to be events the appropriate plan to have return adaptiveand in order to be resilient, to reach new states of adaptive in order to be resilient, to reach new states of adaptive in inorder to be resilient, to reach newhave states of system back toresponse stability. thechanges. EIS will to be equilibrium toMoreover, environment equilibrium response to environment adaptive in inorder to be resilient, to changes. reach new states of Conceive an EIS is a huge challenge due to its complexity equilibrium in response to environment changes. Conceive an EIS a hugetechnology. challenge due its complexity and limitation of is current To to address some of and limitation limitation of is current technology. To to address some of of and of current technology. To address some Conceive an EIS a huge challenge due its complexity these challenges, an architecture for a distributed IMS using these challenges, an architecture for a distributed IMS using these challenges, architecture a distributed IMS using and limitation of an current technology. To address some of Artificial Immune Systems (AIS)forconcepts, called AI2MS, Artificial ImmuneanSystems (AIS)forconcepts, called AI2MS, these challenges, architecture a distributed IMS using was proposed by Zuccolotto (Zuccolotto et al. 2013). The was proposed by Zuccolotto (Zuccolotto et called al. 2013). The Artificial Immune Systems concepts, AI2MS, AI2MS was conceived as a (AIS) multi-agent system, in order to AI2MS was conceived as a multi-agent system, in order to was proposed by Zuccolotto et al. from 2013). The get the distributed, adaptive and(Zuccolotto scalable features AIS. get the distributed, adaptive scalable features AIS. to AI2MS was conceived as aand multi-agent system,from in order Thisthe work presentsadaptive new steps inscalable AI2MSfeatures development, with get distributed, and from AIS. This work presents new steps in AI2MS development, with focus on the collaborative diagnostic adopted in order to use focus on thepresents collaborative diagnostic adopted in order towith use This work new in AI2MS development, information provided bysteps machines in production plant to information provided by machines in production plant to focus onanomalous the collaborative identify events. diagnostic adopted in order to use identify anomalous events. information provided by machines in production plant to This paper is organized as follow: Section 2 presents identify anomalous events. as This paper is organized follow: Section 2 presents concepts about artificial immune algorithms and application, concepts about artificial immune algorithms This is organized as section follow: 2thepresents sectionpaper 3 describes the AI2MS, 4 Section dealand withapplication, role of section 3 describes the AI2MS, section 4 deal with the role of concepts about artificial immune algorithms and application, section by 3 describes theAll AI2MS, section 4 deal with the role of 2405-8963 © 2015, IFAC (International Federation of Automatic Control) Hosting Elsevier Ltd. rights reserved.
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a Collaborative agent and its implementation and section 5 describes a case study. Preliminary results are presented on section 6 and in section 7 conclusion are drawn and future work directions are signalled. 2.
ENGINEERING IMMUNE SYSTEMS (EIS)
The main idea of EIS is to design highly reliable systems that are capable of surviving any disruptions without serious failures, with the capability to resist disturbances while maintaining their stability. EIS is considered an intelligent system that has the ability to assess and predict system performance and health based on available data using different modeling techniques. The focus of engineering immune systems is on mechanical engineering systems and on making use of prognostics and health management techniques in order to reach a mechanical system that has self-immunity (Lee et al. 2011). The idea of EIS was inspired by both biological immune system and the human nervous system and two research fields that could found EIS development are autonomic computing and artificial immune systems. Autonomic computing is defined as computing systems that are capable of managing themselves and adjusting to varying circumstances with minimal human intervention. This vision was initiated by IBM and the idea is analogous to the human autonomic nervous system that is responsible for vital functions in the human body without any conscious recognition or interference from the human being (Kephart & Chess 2003) (Kephart 2005). The autonomic computing paradigm should have some sort of mechanism to controls the state of the system, such that any disruptive change in one of its essential variables would trigger an action in the computing system that brings it back to equilibrium internally and with its environment (Parashar & Hariri 2005).For an autonomic computing system to attain self-management, the system must have four properties: self-configuring, selfhealing, self-optimization and self-protection.
anomalous events or complex self-configurations could be done by cloud-computing. 3.
ARTIFICIAL IMMUNE MAINTENANCE SYSTEM
INTELLIGENT
Artificial Immune Intelligent Maintenance System (AI2MS) is an adaptive, distributed, multi-level IMS proposed to explore the advantages of the distributed nature, pattern recognition and learning capabilities of the AIS. Conceived as a multi-agent system, AI2MS could dynamically change the features of each system component, changing the number of agents, the agent behaviours or introducing new agents. These features allow the development of self-configuration and self-optimization capabilities. AI2MS is composed by several different agents, each providing a specific functionality. Agents were organized in 3 layers: Device, Collaborative and Plant Layers. 3.1 Device Layer Agents Device Layer is the primary layer, provides features of fault detection and device health assessment on a single device. Fig. 1 depicts the agents of this layer and its basic relationships. Device Health Assessment Agent (DHAA) is responsible for the health evaluation of the device and also manages local resources. It can request creation of (New) Fault Detector Agents, Collaborative Agents and requesting maintenance services when needed. Each device has one DHAA, which interacts with all agents in the device and with the Plant Health Assessment Agent, responsible to manage the overall plant maintenance.
The immune system is a remarkable information processing and self-learning system that offers inspiration to build artificial immune system (AIS), that tries to reproduce their strategies to acquire features as self-organisation, learning, memory, adaptation, recognition, robustness and scalability (Dasgupta & Forrest 1999) (Dasgupta et al. 2011). Basic function of immune systems is to recognize cells that are not natural from body, called non-self cells or antigens, and eliminate them. This task is performed by a complex network of specialized cells called lymphocytes. To search for non-self elements lymphocytes produces molecules called antibodies. When and antibody express affinity with an antigen, immune reaction is trigged. Theories that explain this reaction brought inspiration for AIS algorithms (Liu et al. 2006). Following the biological analogy, EIS could be conceived as a multi-layer system, where the ordinary diagnostic/fault detection could be performed locally by adaptive embedded system and task as plan and execute action to mitigate
Fig. 1 - Device layer agents use case Sensors Agents (SA): A SA is linked to a physical sensor that monitors one or more parts of a device and is a source of raw or pre-processed data for other agents.
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Fault Detection Agents (FDA) are responsible for detection specific failure and diagnose of specific parts. A fault pattern detected by FDA is reported to DHAA. New Fault Detection Agents (NFDA) are agents that looking for the presence of new “antigens”, patters which meaning are unknown by the system.
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AI2MS IMPLEMENTATION
The implementation of AI2MS is being carried out using JADE framework, a FIPA compliant MAS platform (Bellifemine et al. 2008). Details about agent model methodology, message exchange and ontology are described in previous work (Zuccolotto, Regal, et al. 2014).
3.2 Collaborative Layer Agents
4.1 Antigens, affinity function and antibodies creation
Agents on collaborative layer are responsible to promote knowledge exchange between devices, managing the training of (N) FDA using shared data set and the classification of new “antibodies”. Fig. 2 Error! Reference source not found.shows the relationships between these agents with DHHA and the maintenance experts. Evolution Agents (EA) are the agent that keeps knowledge about the feature space, e.g., is a specified pattern represents a “antigen” or it is a self-pattern, and this space is partially unknown. EA can also evaluate antibodies in order to define how close they are to known antigens.
Sensor agent produce data associated to one or more physical parts of the equipment. In order to reduce messages size and avoid communication overload, an embedded feature extraction is performed by the SA. To reinforce AIS approach, this data is called Antigen although a better definition should be Antigen Candidate and, in the same way, detectors designed to identify Antigens are called Antibodies. Fig. 3 depicts the characteristic of each one of these elements using a class diagram.
Fig. 3. - Antigen and antibody structures Fig. 2 - Collaborative layer use case Collaborative Agents (CA) are responsible of classifying the unknown antibodies, e.g., antibodies belonging to NFDA that find an antigen. This is performed using different techniques: send the antibody found to other similar devices, collecting data about detected antibodies these devices, evaluating the current classification of the antibody with help of the EA. Result of this classification is presented to an expert and incorporated in to the system knowledge.
Feature class presented in Fig. 3 define the data format, Affinity class implements affinity function. Antibody class keeps the information of category (Know, Unknown, Mutated) and detection flag. Mutation method is used to generate new antibodies derived from specific one. Affinity is measured by the Euclidian distance and the antibody generation, performed by Evolution Agent, is a modification of Variable Radius Negative Selection Algorithm (VRNSA), described in (Zuccolotto, Fasanotti, et al. 2014).
3.3 Plant Layer Agents 4.2 Exploring featuring space Agents on plant layer are responsible overall management of the system. Main component of this layer is the Plant Health Assessment Agent (PHAA) that is responsible to estimate the health conditions of the entire plant. The Failure Mode Update Agent acts as a vaccine, to support equipment installed in poor network condition areas and the Update Training Agent collects sensor data and transfer them to a database, improving training data sets and allowing big data applications.
Evolution agent uses a previously acquired dataset to build the feature space that represents the knowledge of about equipment fault modes as well as about the normal behaviour. To improve this knowledge during operation, feature space exploration is performed by NewFaultMode Agents, using a strategy based on clonal selection algorithms. NFDA agents (and its antibodies) are created by antibody mutation or by a fully random process. NFDA has a limited time life that is increased at each hit detection.
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Creation of NFDA derived from FDA detection is presented on Fig.4. The antigen detected by FDA is reported to DHAA that reports to Plant Agent and requests to Evolution Agent the creation of a New Fault detection derived from the antigen detected. Main goal of this approach is to explore the feature space regarding the identified fault mode found.
Fig. 6 - Collaborative search Detection of these distributed antibodies starts the categorization process presented on Fig. 7. Each antibody detected is classified against each fault/normal mode by Evolution Agent. Possible new fault mode is also evaluated by Collaborative Agent using classification algorithms through a method called CollaborativeClassification.
Fig.4 - FDA detection and NFDA creation When a NewFaultDetection agent hit an antigen, a collaborative process is fired. Other devices from the same type are searched and in case some are found, a comparison of the antibodies is performed. Fig. 5 depicts this process.
Fig. 7 - Categorization and incorporation process Fig. 5 - NFDA detection After receiving a report from NFDA, DHAA creates a new NFDA (depending on resource availability) and requests a search process to Collaborative Agent. 4.3 Collaborative diagnostic Collaborative search is the first stage of collaborative diagnostic. The antibody, found in one device, is propagated to similar devices in the network by the collaborative agent, as shown in Fig. 6. Besides the request for agent creation, Collaborative agent also collects detection's reports from DHAA, used to classify the new antibodies.
Final evaluation is reported to maintenance expert, who have the ultimate decision. Final results are added to knowledge base by the Evolution Agent. The first metric chosen to implement the Collaborative classification method is the Mahalanobis distance (Xiang et al. 2008). Mahalanobis distance accounts for unequal variances as well as correlations between features and could adequately evaluate the distance by assigning different weights or importance factors to the features of data points, improving performance of clustering algorithms In order to classify an antigen, the index NMd, described in (1) was used.
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𝑁𝑁𝑁𝑁𝑁𝑁(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔) =
𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎, ̅̅̅̅̅̅̅̅) 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔)
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represented by red circles, also marking the closest border points or each group.
(1)
̅̅̅̅̅̅̅̅) is the Mahalanobis distance Where 𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 between antigen and the mean value of features of a group (Normal, Fault-n) and ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 𝑚𝑚𝑚𝑚ℎ𝑎𝑎𝑎𝑎(𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔)is the mean Mahalanobis distance of each group component.
Table 1 to Table 3 presents the system detection report for each scenario in a format called confusion matrix. Confusion Matrix shows in the diagonal the positive detection hits and negative detection on the other cells.
A threshold value is used to select a target antigen as belonging to a group and is defined as the maximum NMd (antig, group) calculated over all group samples. Combined action of NFDA, exploring the feature space, and the CA, spreading NFDA trough the equipment network and classifying these new “antibodies” adapts the AI2MS detector set to changes in operation and environmental conditions. Incorporating this information on the health assessment algorithm (performed by DHAA) and using this metric to modify equipment operation configuration (to extend the RUL) or reschedule maintenance (to restore equipment’s health) could give resilience capabilities to the production system. 5.
CASE STUDY
As a case study for this work is an electric valve actuator model CS06 from the Brazilian company Coester was selected. Test bench and data acquisition systems developed for this products are presented in (Zuccolotto, Fasanotti, et al. 2014).
Fig. 8 – Coester actuator feature space Table 1 – Detection report for Test Set A
Data acquired was classified in three groups, Normal (75 cycles samples), Fault 1(gear wear - 50 cycles samples) and Fault 2(gear broken 25 cycles samples). As feature selection algorithm, Wavelet Packet Energy (WPE) with Mother-wavelet Daubechies 6 was implemented due to the satisfactory results achieved in previous works (Gonçalves et al. 2011) and (Piccoli et al. 2012). The WPE coefficients 1 and 6 were selected as significant features. Initial tests were run to evaluate the performance of fault detectors and collaborative classification. First experimental setup was done using one computer, on which three JADE containers were created, each running one device layer. Evolution agents and Collaborative agents are running on main container. Sensor agents get data directly from files. Test sets were created through random choice of each group, 10 cycles from Normal, 10 cycles from Fault1 and 5 cycles from Fault2. Training sets were generated with fixed length of 20 cycles, in three scenarios: A: Normal + Fault1 + Fault2; B: Normal + Fault1; C: Normal + Fault2. This organization allows the use of Fault2 on scenario B and Fault1 on scenario C as unknown antigens, to test the collaborative diagnostic.
C l a s
Feature space is presented in Fig. 8. Normal behaviour features are represented by blue dots, Fault 1 by green and fault 2 by brow dots. Space covered by each detector is
Total Normal Fault1 Fault2 Unknown Accuracy : 99,3
Fault1 60
Fault2 30
59 (98,3%) 30 1 (1,7 %)
Table 2 – Detection report for Test Set B
C l a s
Test Set B Normal 60 59 (98,3%) 1 (1,7%)
Total Normal Fault1 NewGroup Unknown Accuracy : 98,7 %
Fault1 60
Fault2 30
60 29(96,7%) 1 (3,3%)
Table 3 – Detection report for Test Set C
6. RESULTS Six Test set were generated for each scenario, with a total amount of 150 signals. Each device runs 2 Test sets.
Test Set A Normal 60 60
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Test Set C Normal Fault1 Total 60 60 Normal 60 NewGroup 32 (53,3%) Fault2 25 (41,7%) Unknown 3 Accuracy : 81,3%
Fault2 30
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7. CONCLUSIONS This paper deals with the concept of Engineering Immune Systems and describes the preliminary results in one stage of the development of the distributed IMS based on Artificial Immune Systems (AIS) that could compose an EIS. A collaborative procedure was proposed to classify detected patterns which categorization was previously unknown. Current artificial immune algorithms explore the idea of trying different detectors on the same training set. Collaborative diagnose applies the same detector with different signal sources in real-time operation. A possible analogy is the physician that identifies the same symptom is different patients, leading to the same root cause. This work evaluates the accuracy of proposed methodology and metrics adopted. The overall accuracy was 93,1% , similar to other AIS proposals (Laurentys et al. 2010), pointing to a promising solution. Worst result was obtained in scenario C, where Fault1 was absent. In this specific case the detectors size, defined by the distance between clusters, is larger than the other cases, increasing the number of misclassification. In this case, Fault2 detectors hits features related to Fault1. The following activities will be carried out the further development of our proposal:
Analysis of the intensity and quality of the message exchange will be included, in order to evaluate the requirements to the network support and the possibility of integration with the plant control network.
Implementation of dynamic change of agent methods, to allow dynamic system reconfiguration.
Adaptive Clustering as Collaborative Classification Method will be evaluated.
ACKNOWLEDGMENT This work is part of a collaborative research activity between UFRGS and University of Bergamo within the ProSSaLiC Project, funded by European Community’s FP7/2007-2013 under grant agreement n° PIRSES-GA-2010-269322. This work is also supported by CAPES Brazilian Research Agency under contract 99999.008167/2014-01. REFERENCES Bellifemine, F. et al., 2008. JADE: A software framework for developing multi-agent applications. Lessons learned. Information and Software Technology, 50(1-2), pp.10– 21. Dasgupta, D. & Forrest, S., 1999. Artificial immune systems in industrial applications. In Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM’99. IEEE, pp. 257– 267 vol.1.
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