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Article

An Explainable AI-Based Fault Diagnosis Model for Bearings

1
Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea
2
Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: Francesc Pozo
Sensors 2021, 21(12), 4070; https://doi.org/10.3390/s21124070
Received: 25 May 2021 / Revised: 8 June 2021 / Accepted: 11 June 2021 / Published: 13 June 2021
(This article belongs to the Special Issue Sensing Technologies for Fault Diagnostics and Prognosis)
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 explanation 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 explainability is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorporating 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 demonstrated 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. View Full-Text
Keywords: bearing; Boruta; condition-based monitoring; explainable AI; fault diagnosis; model interpretability; SHAP; Stockwell transform bearing; Boruta; condition-based monitoring; explainable AI; fault diagnosis; model interpretability; SHAP; Stockwell transform
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MDPI and ACS Style

Hasan, M.J.; Sohaib, M.; Kim, J.-M. An Explainable AI-Based Fault Diagnosis Model for Bearings. Sensors 2021, 21, 4070. https://doi.org/10.3390/s21124070

AMA Style

Hasan MJ, Sohaib M, Kim J-M. An Explainable AI-Based Fault Diagnosis Model for Bearings. Sensors. 2021; 21(12):4070. https://doi.org/10.3390/s21124070

Chicago/Turabian Style

Hasan, Md J., Muhammad Sohaib, and Jong-Myon Kim. 2021. "An Explainable AI-Based Fault Diagnosis Model for Bearings" Sensors 21, no. 12: 4070. https://doi.org/10.3390/s21124070

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