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

Han Yun Chen, Ching Hung Lee*

*此作品的通信作者

研究成果: Article同行評審

33 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號9131692
頁(從 - 到)134246-134256
頁數11
期刊IEEE Access
8
DOIs
出版狀態Published - 2020

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