Dr Md Junayed Hasan j.hasan@rgu.ac.uk
Research Fellow A
An explainable AI-based fault diagnosis model for bearings.
Hasan, Md. Junayed; Sohaib, Muhammad; Kim, Jong-Myon
Authors
Muhammad Sohaib
Jong-Myon Kim
Abstract
In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for variable speed and load conditions, (2) a statistical feature extraction method is introduced to capture the significance from the invariant pattern of the analyzed data by STC, (3) an explainable feature selection process is proposed by introducing a wrapper-based feature selector—Boruta, (4) a feature filtration method is considered on the top of the feature selector to avoid the multicollinearity problem, and finally, (5) an additive Shapley ex-planation followed by k-NN is proposed to diagnose and to explain the individual decision of the k-NN classifier for debugging the performance of the diagnosis model. Thus, the idea of explaina-bility is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorpo-rating explainability to the feature selection process, and (b) interpretation of the classifier performance with respect to the selected features. The effectiveness of the proposed model is demon-strated on two different datasets obtained from separate bearing testbeds. Lastly, an assessment of several state-of-the-art fault diagnosis algorithms in rotating machinery is included.
Citation
HASAN, M.J., SOHAIB, M. and KIM, J.-M. 2021. An explainable AI-based fault diagnosis model for bearings. Sensors [online], 21(12): sensing technologies for fault diagnostics and prognosis, article 4070. Available from: https://doi.org/10.3390/s21124070
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 11, 2021 |
Online Publication Date | Jun 13, 2021 |
Publication Date | Jun 30, 2021 |
Deposit Date | May 13, 2022 |
Publicly Available Date | May 30, 2022 |
Journal | Sensors |
Print ISSN | 1424-8220 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 21 |
Issue | 12 |
Article Number | 4070 |
DOI | https://doi.org/10.3390/s21124070 |
Keywords | Bearing; Boruta; Condition-based monitoring; Explainable AI; Fault diagnosis; Model interpretability; SHAP; Stockwell transform |
Public URL | https://rgu-repository.worktribe.com/output/1664562 |
Files
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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