Journal
IEEE ACCESS
Volume 8, Issue -, Pages 134246-134256Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3006491
Keywords
Convolutional neural network; vibration signal; explainable AI; fault diagnosis
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Funding
- Ministry of Science and Technology, Taiwan [MOST-109-2634-F-005-004, 108-2634-F-005-001, 107-2634-F-005-001, 108-2218-E-150-004, 106-2221-E-005-010-MY3]
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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.
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