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Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case

Lukas Felsberger 1, 2 Andrea Apollonio 3 Thomas Cartier-Michaud 3 Andreas Müller 4 Benjamin Todd 3 Dieter Kranzlmüller 1 
1 MNM-Team - Munich Network Management Team
TUM - Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne], LMU - Ludwig-Maximilians-Universität München
Abstract : Sophisticated infrastructures often exhibit misbehaviour and failures resulting from complex interactions of their constituent subsystems. Such infrastructures use alarms, event and fault information, which is recorded to help diagnose and repair failure conditions by operations experts. This data can be analysed using explainable artificial intelligence to attempt to reveal precursors and eventual root causes. The proposed method is first applied to synthetic data in order to prove functionality. With synthetic data the framework makes extremely precise predictions and root causes can be identified correctly. Subsequently, the method is applied to real data from a complex particle accelerator system. In the real data setting, deep learning models produce accurate predictive models from less than ten error examples when precursors are captured. The approach described herein is a potentially valuable tool for operations experts to identify precursors in complex infrastructures.
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Submitted on : Thursday, November 4, 2021 - 3:57:14 PM
Last modification on : Thursday, October 13, 2022 - 5:22:09 PM
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Lukas Felsberger, Andrea Apollonio, Thomas Cartier-Michaud, Andreas Müller, Benjamin Todd, et al.. Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case. 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.139-158, ⟨10.1007/978-3-030-57321-8_8⟩. ⟨hal-03414728⟩



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