{"title":"Validating Condition-Based Maintenance Algorithms Through Simulation","authors":"Marcel Chevalier, L\u00e9o Dupont, Sylvain Mari\u00e9, Fr\u00e9d\u00e9rique Roffet, Elena Stolyarova, William Templier, Costin Vasile","volume":195,"journal":"International Journal of Industrial and Systems Engineering","pagesStart":246,"pagesEnd":253,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10013004","abstract":"<p>Industrial end users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both Machine Learning and First Principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed from breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems and humans \u2013 including asset maintenance operations \u2013 in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.<\/p>","references":"[1]\tChevalier, M., S. Mari\u00e9, B. Boguslawski, M. Cercueil, F. Chupot, A. Vignon, and W. Youssef. 2021. \u201cCombining First Principles and Machine Learning for optimal maintenance of electrical assets\u201d. In CIGI QUALITA 2021. May 2021, Grenoble, France.\r\n[2]\tCurreri F., G. Fiumara, and M. Xibilia. 2020. \u201cInput Selection Methods for Soft Sensor Design: A Survey\u201d. Future Internet vol. 12, no. 6: 97. https:\/\/doi.org\/10.3390\/fi12060097.\r\n[3]\tDelange, M., R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, ... and T. Tuytelaars. 2021. \u201cA continual learning survey: Defying forgetting in classification tasks\u201d. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109\/TPAMI.2021.3057446\r\n[4]\tCrawley D. B. and K. L. Lawrie. 2019. \u201cShould We Be Using Just \u2018Typical\u2019 Weather Data in Building Performance Simulation?\u201d. In 16th IBPSA Conference, 2019. https:\/\/climate.onebuilding.org\r\n[5]\tGao, T., B. Boguslawski, S. Mari\u00e9, P. B\u00e9guery, S. Thebault, and S. Lecoeuche. 2019. \u201cData mining and data-driven modelling for Air Handling Unit fault detection\u201d. In E3S Web Conf., Volume 111, CLIMA 2019 Congress. Bucharest, Romania.\r\n[6]\tHe, Y. and B. Sick. 2021. \u201cCLeaR: An adaptive continual learning framework for regression tasks\u201d. AI Perspectives vol. 3(1), pp. 1-16.\r\n[7]\tKadlec, P., R. Grbi\u0107 and B. Gabrys. 2011. \u201cReview of adaptation mechanisms for data-driven soft sensors\u201d. Computers & chemical engineering vol.35(1), pp. 1-24.\r\n[8]\tKegel. L, M. Hahmann, and W. Lehner. 2017. \u201cGenerating What-If Scenarios for Time Series Data\u201d. In Proceedings of the 29th International Conference on Scientific and Statistical Database Management (SSDBM '17). Association for Computing Machinery, New York, NY, USA, Article 3, pp. 1\u201312.\r\n[9]\tMasterpact MTZ Maintenance guide, 2015 https:\/\/www.se.com\/ww\/en\/download\/document\/0613IB1202\/\r\n[10]\tParisi, G. I., R. Kemker, J. L. Part, C. Kanan and S. Wermter. 2019. \u201cContinual lifelong learning with neural networks: A review\u201d. Neural Networks vol. 113, pp. 54-71. ISSN 0893-6080\r\n[11]\t\tRizzi S. (2009) What-If Analysis. In: LIU L., \u00d6ZSU M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https:\/\/doi.org\/10.1007\/978-0-387-39940-9_466\r\n[12]\tSchneider Electric, 2021, https:\/\/www.se.com\/ww\/en\/work\/services\/\r\nservice-plan\/ecostruxure-service-plan.jsp\r\n[13]\tSouza, F. A., R. Ara\u00fajo and J. Mendes. 2016. \u201cReview of soft sensor methods for regression applications\u201d. Chemometrics and Intelligent Laboratory Systems vol. 152, pp. 69-79.\r\n[14]\tCarino, J. A., et al. 2018. \"Fault Detection and Identification Methodology Under an Incremental Learning Framework Applied to Industrial Machinery,\" in IEEE Access, vol. 6, pp. 49755-49766.\r\n[15]\tYu, Y., Peng, M., Wang, H., Ma, Z., Cheng, S., & Renyi, X. 2021. \u201cA continuous learning monitoring strategy for multi-condition of nuclear power plant\u201d. Annals of Nuclear Energy, 164, 108544.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 195, 2023"}