Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis

This study introduces an explainable artificial intelligence (XAI) approach of convolutional neural networks (CNNs) for classification in vibration signals analysis. First, vibration signals are transformed into images by short-time Fourier transform (STFT). A CNN is applied as classification model,...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.134246-134256
Main Authors: Chen, Han-Yun, Lee, Ching-Hung
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial intelligence
Artificial neural networks
Contracts
Convolutional neural network
Decision trees
explainable AI
Explainable artificial intelligence
Fault diagnosis
Feature extraction
Fourier transforms
Frequency-domain analysis
Fuzzy logic
Image classification
Machine learning
Neural networks
Signal analysis
Signal classification
Vibration analysis
vibration signal
Vibrations
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Summary:This study introduces an explainable artificial intelligence (XAI) approach of convolutional neural networks (CNNs) for classification in vibration signals analysis. First, vibration signals are transformed into images by short-time Fourier transform (STFT). A CNN is applied as classification model, and Gradient class activation mapping (Grad-CAM) is utilized to generate the attention of model. By analyzing the attentions, the explanation of classification models for vibration signals analysis can be carried out. Finally, the verifications of attention are introduced by neural networks, adaptive network-based fuzzy inference system (ANFIS), and decision trees to demonstrate the proposed results. By the proposed methodology, the explanation of model using highlighted attentions is carried out.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3006491