A. Aamodt and E. Plaza, Case-based reasoning : Foundational issues, methodological variations, and system approaches, AI communications, vol.7, issue.1, pp.39-59, 1994.

S. Abele and M. Weyrich, Decision support for joint test and diagnosis of production systems based on a concept of shared knowledge, vol.50, pp.15227-15232, 2017.

D. W. Aha, L. A. Breslow, and H. Muñoz-avila, Conversational case-based reasoning, Applied Intelligence, vol.14, issue.1, pp.9-32, 2001.

D. W. Aha, D. Kibler, A. , and M. K. , Instance-based learning algorithms, Machine learning, vol.6, issue.1, pp.37-66, 1991.

S. Al-dahidi, F. Di-maio, P. Baraldi, and E. Zio, Ensemble clustering for fault diagnosis in industrial plants, Chemical Engineering Transactions, vol.43, pp.1225-1230, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01340593

C. Aldrich and L. Auret, Unsupervised process monitoring and fault diagnosis with machine learning methods, 2013.

R. Ali, A. M. Khatak, F. Chow, and S. Lee, A case-based meta-learning and reasoning framework for classifiers selection, Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication, p.31, 2018.

E. Alpaydin, Introduction to machine learning, 2014.

K. Althoff, Case-based reasoning, Handbook of Software Engineering and Knowledge Engineering : Volume I : Fundamentals, pp.549-587, 2001.
URL : https://hal.archives-ouvertes.fr/hal-01764181

P. J. Antsaklis and X. D. Koutsoukos, Hybrid systems : Review and recent progress. Software-Enabled Control : Information Technology for Dynamical Systems, vol.273, pp.298-142, 2003.

A. F. Araújo, M. L. Varela, M. S. Gomes, R. C. Barreto, and J. Trojanowska, Development of an intelligent and automated system for lean industrial production, adding maximum productivity and efficiency in the production process, Advances in Manufacturing, pp.131-140, 2018.

S. Arlot and A. Celisse, A survey of cross-validation procedures for model selection, Statistics surveys, vol.4, pp.40-79, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00407906

A. Azadeh, M. Saberi, A. Kazem, V. Ebrahimipour, A. Nourmohammadzadeh et al., A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ann and support vector machine with hyper-parameters optimization, Applied Soft Computing, vol.13, issue.3, pp.1478-1485, 2013.

M. Bakkari, A. Rachidi, and A. Khatory, Evolution of automated production systems in smes : what are the consequences for the employees ?, Xème Conférence Internationale : Conception et Production Intégrées, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01260773

V. Barbaros, H. Van-hoof, A. Abdolmaleki, and D. Megerl, Eager and memorybased non-parametric stochastic search methods for learning control, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp.1-9, 2018.

M. Behbahani, A. Saghaee, and R. Noorossana, A case-based reasoning system development for statistical process control : Case representation and retrieval, Computers & Industrial Engineering, vol.63, issue.4, pp.1107-1117, 2012.

F. B. Ben-hmida, . Université-bordeaux-i-;-université-de-la, and T. Manouba, Evaluation des performances des systemes multi-agents, 2013.
URL : https://hal.archives-ouvertes.fr/tel-01101049

F. B. Ben-hmida, W. L. Chaari, and M. Tagina, Evaluation of organization in multiagent systems for fault detection and isolation, Agent and Multi-Agent Systems : Technologies and Applications, pp.69-79, 2015.

N. Ben-rabah, R. Saddem, F. B. Hmida, V. Carre-menetrier, and M. Tagina, Intelligent case based decision support system for online diagnosis of automated production system, Journal of Physics : Conference Series, vol.783, p.12009, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01899933

N. B. Ben-rabah, R. Saddem, F. B. Hmida, V. Carre-menetrier, and M. Tagina, Approche originale utilisant le raisonnement à partir de cas pour le diagnostic en ligne des systèmes automatisés de production, 2017.

N. B. Ben-rabah, R. Saddem, F. B. Hmida, V. Carré-ménétrier, and M. Tagina, Automatic acquisition and update of a causal temporal signatures base-for faults diagnosis in automated production systems, In ICINCO, issue.1, pp.262-269, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01899937

R. Bergmann, Experience management : foundations, development methodology, and internet-based applications, 2002.

R. Bergmann and Y. Gil, Similarity assessment and efficient retrieval of semantic workflows, Information Systems, vol.40, pp.115-127, 2014.

J. Berkson, Application of the logistic function to bio-assay, Journal of the American Statistical Association, vol.39, issue.227, pp.357-365, 1944.

J. Berkson, Why i prefer logits to probits, Biometrics, vol.7, issue.4, pp.327-339, 1951.

A. F. Berman, G. S. Maltugueva, and A. Y. Yurin, Application of case-based reasoning and multi-criteria decision-making methods for material selection in petrochemistry, Proceedings of the Institution of Mechanical Engineers, vol.232, pp.204-212, 2018.

O. Bertrand, Détection d'activités par un système de reconnaissance de chroniques et application au cas des simulations distribuées HLA, 2009.

O. Bertrand, P. Carle, and C. Choppy, Chronicle modelling using automata and colored petri nets, The 18th International Workshop on Principles of Diagnosis (DX-07), pp.229-234, 2007.

G. Bisson, La similarité : une notion symbolique/numérique. Apprentissage symbolique-numérique, vol.2, pp.169-201, 2000.

L. Bottou, From machine learning to machine reasoning, Machine learning, vol.94, issue.2, pp.133-149, 2014.

A. Boufaied, Contribution à la surveillance distribuée des systèmes à événements discrets complexes, 2003.

J. Boulanger, Safety of Computer Architectures, 2013.

R. Breil, D. Delahaye, L. Lapasset, and E. Féron, Multi-agent systems to help managing air traffic structure, pp.1-30, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01567694

L. Breiman, Classification and regression trees, 1984.

L. Breiman, Bagging predictors. Machine learning, vol.24, pp.123-140, 1996.

L. Breiman, Some properties of splitting criteria, Machine Learning, vol.24, pp.41-47, 1996.

L. Breiman, Pasting small votes for classification in large databases and on-line, Machine learning, vol.36, issue.1-2, pp.85-103, 1999.

L. Breiman, Random forests. Machine learning, vol.45, pp.5-32, 2001.

H. Brighton and C. Mellish, Advances in instance selection for instance-based learning algorithms. Data mining and knowledge discovery, vol.6, pp.153-172, 2002.

D. Brown, A. Aldea, R. Harrison, C. Martin, and I. Bayley, Temporal casebased reasoning for type 1 diabetes mellitus bolus insulin decision support, Artificial intelligence in medicine, 2017.

P. Carle, C. Choppy, and R. Kervarc, Behaviour recognition using chronicles, Fifth IEEE International Conference on Theoretical Aspects of Software Engineering, pp.100-107, 2011.

C. G. Cassandras and S. Lafortune, Introduction to discrete event systems, Springer Science & Business Media, 2009.

M. T. Cazzolato, A. F. Costa, G. Blanco, J. F. Rodrigues, A. J. Traina et al., Fire detection from social media images by means of instancebased learning, Enterprise Information Systems : 17th International Conference, ICEIS 2015, vol.241, p.23, 2015.

F. E. Cellier and E. Kofman, Continuous system simulation, 2006.

S. Cha, Comprehensive survey on distance/similarity measures between probability density functions, City, vol.1, issue.2, p.1, 2007.

S. Chen, J. Yi, H. Jiang, and X. Zhu, Ontology and cbr based automated decision-making method for the disassembly of mechanical products, Advanced Engineering Informatics, vol.30, issue.3, pp.564-584, 2016.

R. Chougule, D. Rajpathak, and P. Bandyopadhyay, An integrated framework for effective service and repair in the automotive domain : An application of association mining andcase-based-reasoning, Computers in Industry, vol.62, issue.7, pp.742-754, 2011.

K. L. Choy, K. Y. Siu, T. S. Ho, C. Wu, H. Y. Lam et al., An intelligent case-based knowledge management system for quality improvement in nursing homes, VINE Journal of Information and Knowledge Management Systems, vol.48, issue.1, pp.103-121, 2018.

M. Chu, X. Liu, R. Gong, and L. Liu, Multi-class classification method using twin support vector machines with multi-information for steel surface defects, vol.176, pp.108-118, 2018.

J. Cojan and J. Lieber, Applying belief revision to case-based reasoning, Computational Approaches to Analogical Reasoning : Current Trends, pp.133-161, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01095344

M. Combacau, P. Berruet, E. Zamai, P. Charbonnaud, and A. Khatab, Supervision and monitoring of production systems, IFAC Proceedings Volumes, vol.33, pp.849-854, 2000.

I. E. Commission, Plcs-part 3 : programming languages, 1993.

M. Cordier and C. Dousson, Alarm driven monitoring based on chronicles, IFAC Proceedings Volumes, vol.33, pp.291-296, 2000.

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE transactions on information theory, vol.13, issue.1, pp.21-27, 1967.

D. Cram, B. Mathern, and A. Mille, A complete chronicle discovery approach : application to activity analysis, Expert Systems, vol.29, issue.4, pp.321-346, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01354577

M. Danancher, M. Roth, J. Lesage, and L. Litz, Diagnostic des sed basé sur un modele : trois approches évaluées sur une même étude de cas, 4èmes Journées Doctorales/Journées Nationales MACS (JD-JN-MACS'11), pp.165-170, 2011.

B. Darkhovski and M. Staroswiecki, A game-theoretic approach to decision in fdi, IEEE Transactions on Automatic Control, vol.48, issue.5, pp.853-858, 2003.

A. De-haro-garcía, J. Pérez-rodríguez, and N. García-pedrajas, Combining three strategies for evolutionary instance selection for instance-based learning. Swarm and Evolutionary Computation, 2018.

J. De-kleer and B. C. Williams, Diagnosing multiple faults, Artificial intelligence, vol.32, issue.1, pp.97-130, 1987.

P. De-loor, R. Bénard, and P. Chevaillier, Real-time retrieval for case-based reasoning in interactive multiagent-based simulations, Expert Systems with Applications, vol.38, issue.5, pp.5145-5153, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00608970

R. L. De-mantaras, D. Mcsherry, D. Bridge, D. Leake, B. Smyth et al., Retrieval, reuse, revision and retention in case-based reasoning, The Knowledge Engineering Review, vol.20, issue.3, pp.215-240, 2005.

R. Debouk, S. Lafortune, and D. Teneketzis, Coordinated decentralized protocols for failure diagnosis of discrete event systems, Discrete Event Dynamic Systems, vol.10, issue.1-2, pp.33-86, 2000.

N. Dendani, M. Khadir, and S. Guessoum, Hybrid approach for fault diagnosis based on cbr and ontology : using jcolibri framework, Complex Systems (ICCS), 2012 International Conference on, pp.1-8, 2012.

H. Derbel, Diagnostic à base de modèles des systèmes temporisés et d'une sous-classe de systèmes dynamiques hybrides, 2009.

B. Diaz-agudo and A. K. Goel, Report on the 24th international conference on case-based reasoning research and development (iccbr-2016). AI Magazine, vol.38, pp.89-90, 2017.

F. M. Donini, M. Lenzerini, D. Nardi, and A. Schaerf, Reasoning in description logics, Principles of knowledge representation, vol.1, pp.191-236, 1996.

D. Dou and S. Zhou, Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery, Applied Soft Computing, vol.46, pp.459-468, 2016.

C. Dousson, F. Clerot, and F. Fessant, Method for the machine learning of frequent chronicles in an alarm log for the monitoring of dynamic systems, US Patent, vol.7, p.482, 2008.

C. Dousson, P. Gaborit, and M. Ghallab, Situation recognition : representation and algorithms, IJCAI, vol.93, pp.166-172, 1993.

C. Dousson and M. Ghallab, Suivi et reconnaissance de chroniques. Revue d'intelligence artificielle, vol.8, pp.29-61, 1994.

C. Dousson, K. Pentikousis, T. Sutinen, and J. Makela, Chronicle recognition for mobility management triggers, Computers and Communications, pp.305-310, 2007.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, 2012.

T. Efraim, E. A. Jay, T. Liang, and R. Mccarthy, Decision support systems and intelligent systems, 2001.

P. Erard and P. Déguénon, Simulation par événements discrets, 1996.

O. Fakhfakh, Flow monitoring and diagnosis of cyclic production workshops, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01742476

M. P. Fanti and C. Seatzu, Fault diagnosis and identification of discrete event systems using petri nets, WODES 2008. 9th International Workshop on, p.147, 2008.

J. Ferber and G. Weiss, Multi-agent systems : an introduction to distributed artificial intelligence, vol.1, 1999.

R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of human genetics, vol.7, issue.2, pp.179-188, 1936.

O. Fournier, Conception de la commande d'un système automatisé de production : Apport des graphes et de l'ordonnancement cyclique, 2002.

Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of computer and system sciences, vol.55, issue.1, pp.119-139, 1997.

Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, Icml, vol.96, pp.148-156, 1996.

D. Gacquer, V. Delcroix, F. Delmotte, and S. Piechowiak, Comparative study of supervised classification algorithms for the detection of atmospheric pollution, Engineering Applications of Artificial Intelligence, vol.24, issue.6, pp.1070-1083, 2011.

Y. Gao, Z. Shang, and A. Kokossis, Agent-based intelligent system development for decision support in chemical process industry, Expert Systems with Applications, vol.36, issue.8, pp.11099-11107, 2009.

Z. Gao, C. Cecati, and S. X. Ding, A survey of fault diagnosis and fault-tolerant techniques-part i : Fault diagnosis with model-based and signal-based approaches, IEEE Transactions on Industrial Electronics, vol.62, issue.6, pp.3757-3767, 2015.

A. Géron, Machine Learning avec Scikit-Learn : Mise en oeuvre et cas concrets. Hors collection. Dunod, 2017.

M. P. Groover, Automation, production systems, and computer-integrated manufacturing, 2007.

B. Guerraz and C. Dousson, Chronicles construction starting from the fault model of the system to diagnose, International Workshop on Principles of Diagnosis (DX04), pp.51-56, 2004.

R. D. Hackathorn and P. G. Keen, Organizational strategies for personal computing in decision support systems, MIS quarterly, pp.21-27, 1981.

F. Hamdi, Contribution à la synthèse d'observateurs pour les systèmes hybrides, 2010.

R. W. Hamming, Error detecting and error correcting codes, Bell Labs Technical Journal, vol.29, issue.2, pp.147-160, 1950.

T. Han, D. Jiang, Q. Zhao, L. Wang, Y. et al., Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery, Transactions of the Institute of Measurement and Control, p.0142331217708242, 2017.

T. Hastie, R. Tibshirani, and J. Friedman, Unsupervised learning, The elements of statistical learning, pp.485-585, 2009.

K. He, M. Jia, and C. Liu, A review of optimal sensor deployment to diagnose manufacturing systems, IEEE Access, 2018.

Y. He, J. Guo, and X. Zheng, From surveillance to digital twin : Challenges and recent advances of signal processing for industrial internet of things, IEEE Signal Processing Magazine, vol.35, issue.5, pp.120-129, 2018.

D. O. Hebb, The organization of behavior : A neuropsychological theory, 1949.

T. Hedberg, A. B. Feeney, and J. Camelio, Toward a diagnostic and prognostic method for knowledge-driven decision-making in smart manufacturing technologies, Disciplinary Convergence in Systems Engineering Research, pp.859-873, 2018.

J. Henriet, P. Leni, R. Laurent, and M. Salomon, Case-based reasoning adaptation of numerical representations of human organs by interpolation, Expert Systems with Applications, vol.41, issue.2, pp.260-266, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00967441

C. W. Holsapple and A. B. Whinston, Decision support systems : a knowledgebased approach, Studies in Informatics and Control, vol.10, issue.1, pp.73-76, 2001.

H. Homayouni and E. G. Mansoori, A novel density-based ensemble learning algorithm with application to protein structural classification, vol.21, pp.167-179, 2017.

T. G. Houeland and A. Aamodt, A learning system based on lazy metareasoning, Progress in Artificial Intelligence, vol.7, issue.2, pp.129-146, 2018.

R. Isermann, Supervision, fault-detection and fault-diagnosis methods-a short introduction, Combustion Engine Diagnosis, pp.25-47, 2017.

N. Japkowicz and S. Stephen, The class imbalance problem : A systematic study. Intelligent data analysis, vol.6, pp.429-449, 2002.

F. Jia, Y. Lei, L. Guo, J. Lin, and S. Xing, A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines, Neurocomputing, vol.272, pp.619-628, 2018.

J. Johansson and M. Cederfeldt, Interactive case based reasoning through visual representation-supporting the reuse of components in variant-rich products, DS 70 : Proceedings of DESIGN 2012, the 12th International Design Conference, 2012.

M. I. Jordan and T. M. Mitchell, Machine learning : Trends, perspectives, and prospects, Science, vol.349, issue.6245, pp.255-260, 2015.

F. Kadri, Contribution à la conception d'un système d'aide à la décision pour la gestion de situations de tension au sein des systèmes hospitaliers. Application à un service d'urgence, 2014.

N. Kim and R. A. Wysk, Consideration of human operators in designing manufacturing systems, In Manufacturing System. InTech, 2012.

I. Kiss, B. Genge, P. Haller, and G. Sebestyén, Data clustering-based anomaly detection in industrial control systems, Intelligent Computer Communication and Processing (ICCP), pp.275-281, 2014.

J. Kolodner, Morgan kaufmann publishers inc, 1993.

J. L. Kolodner, An introduction to case-based reasoning, Artificial intelligence review, vol.6, issue.1, pp.3-34, 1992.

L. Guillou, X. Cordier, M. Robin, S. Rozé, and L. , Chronicles for on-line diagnosis of distributed systems, ECAI, vol.8, pp.194-198, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00461386

D. B. Leake, Case-Based Reasoning : Experiences, lessons and future directions, 1996.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning. nature, vol.521, p.436, 2015.

P. Legendre and L. Legendre, Numerical ecology : second english edition. Developments in environmental modelling, p.20, 1998.

Y. Lei, Z. He, Y. Zi, C. , and X. , New clustering algorithm-based fault diagnosis using compensation distance evaluation technique, Mechanical Systems and Signal Processing, vol.22, issue.2, pp.419-435, 2008.

O. Lejri and M. Tagina, Representation in case-based reasoning applied to control reconfiguration, ICDM, pp.113-120, 2012.

M. Lenz, B. Bartsch-spörl, H. Burkhard, and S. Wess, Case-based reasoning technology : from foundations to applications, vol.1400, 2003.

T. W. Liao, Z. Zhang, and C. R. Mount, Similarity measures for retrieval in case-based reasoning systems, Applied Artificial Intelligence, vol.12, issue.4, pp.267-288, 1998.

J. Lieber, Application of the revision theory to adaptation in case-based reasoning : The conservative adaptation. Case-Based Reasoning Research and Development, pp.239-253, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00189626

R. Liu, B. Yang, E. Zio, C. , and X. , Artificial intelligence for fault diagnosis of rotating machinery : A review, Mechanical Systems and Signal Processing, vol.108, pp.33-47, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01786576

F. Longo, L. Nicoletti, and A. Padovano, Smart operators in industry 4.0 : A human-centered approach to enhance operators' capabilities and competencies within the new smart factory context, Computers & industrial engineering, vol.113, pp.144-159, 2017.

B. Luo, H. Wang, H. Liu, B. Li, and F. Peng, Early fault detection of machine tools based on deep learning and dynamic identification, IEEE Transactions on Industrial Electronics, 2018.

G. Maitre, Y. Pencolé, A. Subias, and H. E. Gougam, Modélisation et analyse de chroniques pour le diagnostic, Modélisation des Systèmes Réactifs, 2015.

S. Markovitch and P. D. Scott, The role of forgetting in learning, Machine Learning Proceedings, pp.459-465, 1988.

P. Mars, Learning algorithms : theory and applications in signal processing, control and communications, 2018.

H. Mejri, Un système d'aide à la régulation d'un réseau de transport multimodal perturbé : réponse au problème de congestion, 2012.

R. Micalizio, E. Scala, and P. Torasso, Intelligent supervision for robust plan execution, Congress of the Italian Association for Artificial Intelligence, pp.151-163, 2011.

R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine learning : An artificial intelligence approach, 2013.

D. Miljkovi?, Fault detection methods : A literature survey, MIPRO, 2011 proceedings of the 34th international convention, pp.750-755, 2011.

S. Minton, Quantitative results concerning the utility of explanation-based learning, Artificial Intelligence, vol.42, issue.2-3, pp.363-391, 1990.

T. M. Mitchell, Does machine learning really work ? AI magazine, vol.18, p.11, 1997.

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning, 2012.

A. Moosavian, H. Ahmadi, A. Tabatabaeefar, and M. Khazaee, Comparison of two classifiers ; k-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing, Shock and Vibration, vol.20, issue.2, pp.263-272, 2013.

A. P. Moreno, O. L. Santiago, J. M. De-lazaro, and E. G. Moreno, Comparative evaluation of classification methods used in fault diagnosis of industrial processes, IEEE Latin America Transactions, vol.11, issue.2, pp.682-689, 2013.

F. Mosannenzadeh, A. Bisello, C. Diamantini, G. Stellin, and D. Vettorato, A case-based learning methodology to predict barriers to implementation of smart and sustainable urban energy projects, Cities, vol.60, pp.28-36, 2017.

K. L. Mosier and L. J. Skitka, 10 human decision makers and automated decision aids : Made for each other ? Automation and human performance : Theory and applications, vol.120, 2018.

N. M. Nasrabadi, Pattern recognition and machine learning, Journal of electronic imaging, vol.16, issue.4, p.49901, 2007.

J. Niguez, S. Amari, and J. Faure, Fault-tolerant control of discrete event systems : Comparison of two approaches on the same case study, Emerging Technologies & Factory Automation (ETFA), 2015 IEEE 20th Conference on, pp.1-4, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01238783

J. Niguez, S. Amari, and J. Faure, Active fault-tolerant control of timed automata with guards, vol.50, pp.13648-13653, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01525062

S. Ocio and J. A. Brugos, Multi-agent systems and sandbox games, 2009.

M. Pacaux-lemoine, D. Trentesaux, and G. Z. Rey, Human-machine cooperation to design intelligent manufacturing systems, Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE, pp.5904-5909, 2016.

M. Pacaux-lemoine, D. Trentesaux, G. Z. Rey, and P. Millot, Designing intelligent manufacturing systems through human-machine cooperation principles : A human-centered approach, Computers & Industrial Engineering, vol.111, pp.581-595, 2017.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn : Machine learning in python, Journal of machine learning research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

J. A. Peixoto, J. A. Oliveira, A. D. Rocha, and C. E. Pereira, The migration from conventional manufacturing systems for multi-agent paradigm : The first step, Doctoral Conference on Computing, Electrical and Industrial Systems, pp.111-118, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01343472

J. Perrin, F. Binet, J. Dumery, C. Merlaud, J. Trichard et al., , 2004.

, Automatique et informatique industrielle : Bases théoriques, méthodologiques et techniques

A. Philippot, Contribution au diagnostic décentralisé des systèmes à événements discrets : Application aux systèmes manufacturiers, 2006.

A. Philippot, P. Marangé, V. Carré-ménétrier, and B. Riera, Implementation of diagnosis approach for discrete event systems, International Symposium on Security and Safety of Complex Systems, 2SCS'12, p.page CDROM, 2012.

R. Pichard, N. B. Rabah, V. Carre-menetrier, and B. Riera, Csp solver for safe plc controller : Application to manufacturing systems, IFAC-PapersOnLine, vol.49, issue.12, pp.402-407, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01899934

H. Pierreval and H. Ralambondrainy, A simulation and learning technique for generating knowledge about manufacturing systems behavior, Journal of the Operational Research Society, vol.41, issue.6, pp.461-474, 1990.
URL : https://hal.archives-ouvertes.fr/hal-02097778

E. Plaza, Cases as terms : A feature term approach to the structured representation of cases, International Conference on Case-Based Reasoning, pp.265-276, 1995.

D. J. Power, A brief history of decision support systems, DSSResources. COM, World Wide Web, 2007.

R. W. Proctor and T. Van-zandt, Human factors in simple and complex systems, 2018.

J. R. Quinlan, Induction of decision trees, Machine learning, vol.1, issue.1, pp.81-106, 1986.

J. R. Quinlan, Bagging, boosting, and c4. 5, AAAI/IAAI, vol.1, pp.725-730, 1996.

R. J. Quinlan, C4. 5 : Programs for machine learning, 1993.

K. Racine and Q. Yang, On the consistency management of large case bases : the case for validation, To appear in AAAI Technical Report-Verification and Validation Workshop, p.1, 1996.

L. Rajaoarisoa and M. Sayed-mouchaweh, Adaptive online fault diagnosis of manufacturing systems based on devs formalism, vol.50, pp.6825-6830, 2017.

R. B. Rao, G. Fung, and R. Rosales, On the dangers of cross-validation. an experimental evaluation, Proceedings of the 2008 SIAM International Conference on Data Mining, pp.588-596, 2008.

R. Reiter, A theory of diagnosis from first principles, Artificial intelligence, vol.32, issue.1, pp.57-95, 1987.

P. Reuss, R. Stram, K. Althoff, W. Henkel, and F. Henning, Knowledge engineering for decision support on diagnosis and maintenance in the aircraft domain, Synergies Between Knowledge Engineering and Software Engineering, pp.173-196, 2018.

E. R. Reyes, S. Negny, G. C. Robles, L. Lann, and J. , Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning : Application to process engineering design, Engineering Applications of Artificial Intelligence, vol.41, pp.1-16, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01879760

B. Riera, R. Benlorhfar, D. Annebicque, F. Gellot, and B. Vigario, Robust control filter for manufacturing systems : application to plc training, 18th World Congress of the International Federation of Automatic Control, 2011.

B. Riera, P. Marangé, F. Gellot, O. Nocent, A. Magalhaes et al., Complementary usage of real and virtual manufacturing systems for safe plc training, IFAC Proceedings Volumes, vol.42, pp.89-94, 2010.

C. K. Riesbeck and R. C. Schank, Inside case-based reasoning, 2013.

A. D. Rocha, P. Lima-monteiro, M. Parreira-rocha, and J. Barata, Artificial immune systems based multi-agent architecture to perform distributed diagnosis, Journal of Intelligent Manufacturing, pp.1-13, 2017.

B. Rohee, Répartition dynamique d'activité sur un automate programmable industriel à moniteur non préhemptif. Mémoire de DEA de Production Automatisé, 2005.

F. Rosenblatt, The perceptron, a perceiving and recognizing automaton Project Para, 1957.

F. Rosenblatt, Principles of neurodynamics. perceptrons and the theory of brain mechanisms, 1961.

P. A. Ruiz, B. Kamsu-foguem, and D. Noyes, Knowledge reuse integrating the collaboration from experts in industrial maintenance management. Knowledge-Based Systems, vol.50, pp.171-186, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00861829

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal representations by error propagation, 1985.

R. Saddem, Diagnosticabilité modulaire appliquée au Diagnostic en ligne des Systèmes Embarqués Logiques, 2012.

R. Saddem, T. Armand, and T. Moncef, Algorithme d'interprétation d'une base de signatures temporelles causales pour le diagnostic en ligne des systèmes à événements discrets, 9th International Conference on Modeling, Optimization & SIMulation, 2012.

R. Saddem and A. Philippot, Causal temporal signature from diagnoser model for online diagnosis of discrete event systems, Control, Decision and Information Technologies, pp.551-556, 2014.

R. Saddem, A. Toguyeni, and M. Tagina, A model-checking approach for checking the Consistency of a set of Causal Temporal Signatures, 9th European Workshop on Advanced Control and Diagnosis, pp.1-6, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00801503

R. Saddem, A. Toguyeni, and M. Tagina, Diagnostic des systèmes embarqués critiques : Application à la carte de commande du système de freinage d'un train, Journal Européen des Systèmes Automatisés (JESA), vol.45, issue.1-3, pp.205-220, 2011.

R. Saddem, A. Toguyeni, and M. Tagina, A model-checking approach for checking the consistency of a set of causal temporal signatures, 9th European Workshop on Advanced Control and Diagnosis, pp.1-6, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00801503

K. Salahshoor, M. Kordestani, and M. S. Khoshro, Fault detection and diagnosis of an industrial steam turbine using fusion of svm (support vector machine) and anfis (adaptive neuro-fuzzy inference system) classifiers. Energy, vol.35, pp.5472-5482, 2010.

M. Salem, G. Lakatos, F. Amirabdollahian, and K. Dautenhahn, Would you trust a (faulty) robot ? : Effects of error, task type and personality on human-robot cooperation and trust, Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp.141-148, 2015.

M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. Teneketzis, Diagnosability of discrete-event systems, IEEE Transactions on automatic control, vol.40, issue.9, pp.1555-1575, 1995.

M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. C. Teneketzis, Failure diagnosis using discrete-event models, IEEE transactions on control systems technology, vol.4, issue.2, pp.105-124, 1996.

. Bibliographie,

A. L. Samuel, Some studies in machine learning using the game of checkers, IBM Journal of research and development, vol.3, issue.3, pp.210-229, 1959.

R. E. Schapire, The boosting approach to machine learning : An overview, Nonlinear estimation and classification, pp.149-171, 2003.

U. Shafi, A. Safi, A. R. Shahid, S. Ziauddin, and M. Q. Saleem, Vehicle remote health monitoring and prognostic maintenance system, Journal of Advanced Transportation, 2018.

S. Shalev-shwartz and S. Ben-david, Understanding machine learning : From theory to algorithms, 2014.

H. Shin, K. Cho, and C. Oh, Svm-based dynamic reconfiguration cps for manufacturing system in industry 4.0. Wireless Communications and Mobile Computing, 2018.

B. Smyth and E. Mckenna, Modelling the competence of case-bases, European Workshop on Advances in Case-Based Reasoning, pp.208-220, 1998.

M. Sokolova and G. Lapalme, A systematic analysis of performance measures for classification tasks, Information Processing & Management, vol.45, issue.4, pp.427-437, 2009.

A. Stahl, Learning of knowledge-intensive similarity measures in case-based reasoning. dissertation. de, 2003.

D. Stodolsky, Steven l. alter : Decision support systems : Current practice and continuing challenges, Behavioral Science, vol.27, issue.1, pp.91-92, 1980.

I. Studnia, Détection d'intrusion pour des réseaux embarqués automobiles : une approche orientée langage, 2015.

R. Su, Distributed diagnosis for discrete-event systems, 2004.

A. Subias, L. Travé-massuyès, L. Corronc, and E. , Learning chronicles signing multiple scenario instances, IFAC Proceedings Volumes, vol.47, pp.10397-10402, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01162866

G. A. Susto, A. Schirru, S. Pampuri, S. Mcloone, and A. Beghi, Machine learning for predictive maintenance : A multiple classifier approach, IEEE Transactions on Industrial Informatics, vol.11, issue.3, pp.812-820, 2015.

J. A. Suykens and J. Vandewalle, Least squares support vector machine classifiers. Neural processing letters, vol.9, pp.293-300, 1999.

F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang et al., Digital twin-driven product design, manufacturing and service with big data, The International Journal of Advanced Manufacturing Technology, vol.94, issue.9, pp.3563-3576, 2018.

F. Tao, Q. Qi, A. Liu, and A. Kusiak, Data-driven smart manufacturing, Journal of Manufacturing Systems, 2018.

K. Tidriri, N. Chatti, S. Verron, and T. Tiplica, Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring : A review of researches and future challenges, Annual Reviews in Control, vol.42, pp.63-81, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01399110

A. Toguyeni, E. Craye, and J. Gentina, A method of temporal analysis to perform online diagnosis in the context of flexible manufacturing system, Industrial Electronics Society, 1990. IECON'90., 16th Annual Conference of IEEE, pp.445-450, 1990.

A. Toguyeni, E. Craye, and J. Gentina, A framework to design a distributed diagnosis in fms, IEEE International Conference on, vol.4, pp.2774-2779, 1996.

A. Toguyeni, E. Craye, and J. Gentina, Time and reasoning for on-line diagnosis of failures in flexible manufacturing systems, Proceedings of the 15th IMACS world congress on scientific computation, modeling, and applied mathematics, vol.6, pp.709-714, 1997.

A. K. Toguyeni, Surveillance et diagnostic en ligne dans les ateliers flexibles de l'industrie manufacturière, vol.1, 1992.

D. Trentesaux, Conception d'un système de pilotage distribué, supervisé et multicritère pour les systèmes automatisés de production, 1996.

D. Trentesaux and P. Millot, A human-centred design to break the myth of the "magic human" in intelligent manufacturing systems, Service orientation in holonic and multi-agent manufacturing, pp.103-113, 2016.

J. Trojanowska, M. L. Varela, and J. Machado, The tool supporting decision making process in area of job-shop scheduling, World Conference on Information Systems and Technologies, pp.490-498, 2017.

W. Van-der-hoek and M. Wooldridge, Multi-agent systems, Foundations of Artificial Intelligence, vol.3, pp.887-928, 2008.

E. Vareilles, M. Aldanondo, A. C. De-boisse, T. Coudert, P. Gaborit et al., How to take into account general and contextual knowledge for interactive aiding design : Towards the coupling of csp and cbr approaches, Engineering Applications of Artificial Intelligence, vol.25, issue.1, pp.31-47, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00760363

P. Vazan, D. Janikova, P. Tanuska, M. Kebisek, and Z. Cervenanska, Using data mining methods for manufacturing process control, IFAC-PapersOnLine, vol.50, issue.1, pp.6178-6183, 2017.

V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, Y. , and K. , A review of process fault detection and diagnosis : Part iii : Process history based methods, 2003.

, Computers & chemical engineering, vol.27, issue.3, pp.327-346

J. Vizcarrondo, J. Aguilar, E. Exposito, A. , and S. , Distributed chronicles to faults recognition, Ciencia e Ingeniería, vol.36, issue.2, pp.73-83, 2015.

B. Vogel-heuser, A. Fay, I. Schaefer, and M. Tichy, Evolution of software in automated production systems : Challenges and research directions, Journal of Systems and Software, vol.110, pp.54-84, 2015.

G. W. Vogl, B. A. Weiss, and M. Helu, A review of diagnostic and prognostic capabilities and best practices for manufacturing, Journal of Intelligent Manufacturing, pp.1-17, 2016.

I. Watson, Case-based reasoning is a methodology not a technology, Research and Development in Expert Systems XV, pp.213-223, 1999.

I. Watson and F. Marir, Case-based reasoning : A review. The knowledge engineering review, vol.9, pp.327-354, 1994.

C. Wei, Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions, Environmental Modelling & Software, vol.63, pp.137-155, 2015.

D. A. Whitaker, D. Egan, E. Obrien, and D. Kinnear, Application of multivariate data analysis to machine power measurements as a means of tool life predictive maintenance for reducing product waste, 2018.

A. Widodo and B. Yang, Support vector machine in machine condition monitoring and fault diagnosis. Mechanical systems and signal processing, vol.21, pp.2560-2574, 2007.

W. Wilke and R. Bergmann, Techniques and knowledge used for adaptation during case-based problem solving, International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp.497-506, 1998.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining : Practical machine learning tools and techniques, 2016.

F. J. Wouda, M. Giuberti, G. Bellusci, and P. H. Veltink, Estimation of full-body poses using only five inertial sensors : an eager or lazy learning approach ?, Sensors, vol.16, issue.12, p.2138, 2016.

C. Wu, F. Liu, and B. Zhu, Control chart pattern recognition using an integrated model based on binary-tree support vector machine, International Journal of Production Research, vol.53, issue.7, pp.2026-2040, 2015.

T. Wuest, D. Weimer, C. Irgens, and K. Thoben, Machine learning in manufacturing : advantages, challenges, and applications. Production & Manufacturing Research, vol.4, pp.23-45, 2016.

N. Xiong, T. Olsson, and P. Funk, Case-based reasoning supports fault diagnosis using sensor information. eMaintenance, p.63, 2012.

A. Yan, H. Yu, W. , and D. , Case-based reasoning classifier based on learning pseudo metric retrieval, Expert Systems with Applications, vol.89, pp.91-98, 2017.

S. Zambal, C. Eitzinger, M. Clarke, J. Klintworth, and P. Mechin, A digital twin for composite parts manufacturing : Effects of defects analysis based on manufacturing data, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), pp.803-808, 2018.

J. Zaytoon, Systèmes dynamiques hybrides. Hermes, 2001.

J. Zaytoon and S. Lafortune, Overview of fault diagnosis methods for discrete event systems, Annual Reviews in Control, vol.37, issue.2, pp.308-320, 2013.

C. Zhang, O. Vinyals, R. Munos, and S. Bengio, A study on overfitting in deep reinforcement learning, 2018.

Y. Zhang, Z. Yang, H. Lu, X. Zhou, P. Phillips et al., Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation, IEEE Access, vol.4, pp.8375-8385, 2016.

M. Zhou, Z. Chen, W. He, C. , and X. , Representing and matching simulation cases : A case-based reasoning approach, Computers & Industrial Engineering, vol.59, issue.1, pp.115-125, 2010.

S. Zidi, SARR : Système d'aide a la régulation et la reconfiguration des réseaux de transport multimodal, vol.1, 2007.