%0 Conference Proceedings
%T Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case
%+ Munich Network Management Team (MNM-Team)
%+ CERN [Genève]
%+ European Organization for Nuclear Research (CERN)
%+ Hochschule Darmstadt
%A Felsberger, Lukas
%A Apollonio, Andrea
%A Cartier-Michaud, Thomas
%A Müller, Andreas
%A Todd, Benjamin
%A Kranzlmüller, Dieter
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Dublin, Ireland
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-12279
%P 139-158
%8 2020-08-25
%D 2020
%R 10.1007/978-3-030-57321-8_8
%K Prognostics and diagnostics
%K Explainable AI
%K Deep learning
%K Multivariate time series
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-03414728/document
%2 https://inria.hal.science/hal-03414728/file/497121_1_En_8_Chapter.pdf
%L hal-03414728
%U https://inria.hal.science/hal-03414728
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-12279