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

Han Yun Chen, Ching Hung Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number9131692
Pages (from-to)134246-134256
Number of pages11
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Convolutional neural network
  • explainable AI
  • fault diagnosis
  • vibration signal

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