Abstract
Cyber-Physical Systems (CPS) transform traditional systems into a network of connected and heterogeneous systems, integrating computational and physical elements, that works as a complex system whose overall properties are greater than the sum of its parts. However, CPS is not free from faulty episodes and their consequences such as malfunctions, breakdowns, and service interruption. Traditional centralized models for fault-tolerance do not meet the complexity of the current industrial scenarios and particularly the industrial CPS requirements. Having this in mind, this work presents a holonic-based architecture to address the fault-tolerance in CPS by distributing the detection, diagnosis, and recovery in the local individual entities and also considers the emergent behaviour resulting from the collaboration of these entities. An experimental case study is used to illustrate the potential application of the fault-tolerant approach.
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References
Barbosa, J., Leitão, P., Adam, E., Trentesaux, D.: Dynamic self-organization in holonic multi-agent manufacturing systems: the ADACOR evolution. Comput. Ind. 16, 99–111 (2015)
Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-agent Systems with JADE. Wiley (2007)
Cholette, M.E., Liu, J., Djurdjanovic, D., Marko, K.A.: Monitoring of complex systems of interacting dynamic systems. Appl. Intell. 37(1), 60–79 (2012)
Deen, S. (ed.): Agent-Based Manufacturing: Advances in the Holonic Approach. Springer Verlag Berlin Heidelberg (2003)
Dowdeswell, B., Sinha, R., MacDonell, S.G.: Finding faults: a scoping study of fault diagnostics for industrial cyber–physical systems. J. Syst. Soft. 168, 110,638 (2020)
Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques-part i: fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 62(6), 3757–3767 (2015)
Greenwood, G.W.: Attaining fault tolerance through self-adaption: the strengths and weaknesses of evolvable hardware approaches. In: IEEE World Congress on Computational Intelligence, pp. 368–387. Springer (2008)
Illias, H.A., Choon, C.K., Liang, W.Z., Mokhlis, H., Ariffin, A.M., Yousof, M.F.M.: Fault identification in power transformers using dissolve gas analysis and support vector machine. In: 2021 IEEE International Conference on the Properties and Applications of Dielectric Materials (ICPADM), pp. 33–36. IEEE (2021)
Johnson, B.W.: An introduction to the design and analysis of fault-tolerant systems. Fault-tolerant Comput. Syst. Des. 1, 1–84 (1996)
Koestler, A.: The Ghost in the Machine. Arkana Books, London (1969)
Lee, E.A.: The past, present and future of cyber-physical systems: a focus on models. Sensors 15(3), 4837–4869 (2015)
Lee, J., Lee, Y.C., Kim, J.T.: Migration from the traditional to the smart factory in the die-casting industry: novel process data acquisition and fault detection based on artificial neural network. J. Mater. Proc. Technol. 290, 116–972 (2021)
Leit\(\tilde{a}\)o, P., Colombo, A., Karnouskos, S.: Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput. Ind. 81, 11–25 (2016)
Leit\(\tilde{a}\)o, P., Restivo, F.: ADACOR: a holonic architecture for agile and adaptive manufacturing control. Comput. Ind. 57, 121–130 (2006)
Leit\(\tilde{a}\)o, P., Mendes, J.M., Colombo, A.W.: Decision support system in a service-oriented control architecture for industrial automation. In: Proceedings of the 13th IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1228–1235 (2008)
Murata, T.: Petri nets: properties, analysis and applications. Proc. IEEE 77(4), 541–580 (1989)
Nelson, L.S.: The shewhart control chart-tests for special causes. J. Qual. Technol. 16(4), 237–239 (1984)
Odrey, N.G.: Error recovery in production systems: a petri net based intelligent system approach. IntechOpen (2008)
Piardi, L., Leitão, P., de Oliveira, A.S.: Fault-tolerance in cyber-physical systems: literature review and challenges. In: 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), vol. 1, pp. 29–34. IEEE (2020)
Rajput, P.K., Sikka, G.: Exploration in adaptiveness to achieve automated fault recovery in self-healing software systems: a review. Int. Decis. Tech. 13(3), 329–341 (2019)
Steinegger, M., Melik-Merkumians, M., Zajc, J., Schitter, G.: A framework for automatic knowledge-based fault detection in industrial conveyor systems. In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation, pp. 1–6. IEEE (2017)
Wen, L., Li, X., Gao, L., Zhang, Y.: A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans. Ind. Electron. 65(7), 5990–5998 (2017)
Wooldridge, M.: Introduction to Multiagent Systems. Wiley, Inc. (2002)
Yan, X., Liu, Y., Jia, M.: Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions. Knowl.-Based Syst. 193, 105–484 (2020)
Yang, C., Zou, Y., Lai, P., Jiang, N.: Data mining-based methods for fault isolation with validated FMEA model ranking. Appl. Intell. 43(4), 913–923 (2015)
Acknowledgements
This work has been supported by FCT—Fundaç\(\tilde{a}\)o para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020. The author Luis Piardi thanks the Fundaç\(\tilde{a}\)o para a Ciência e Tecnologia (FCT), Portugal for the PhD Grant UI/BD/151286/2021.
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Piardi, L., Leitão, P., Costa, P., de Oliveira, A.S. (2022). Fault-Tolerance in Cyber-Physical Systems Using Holonic Multi-agent Systems. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-99108-1_4
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