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  4. Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation
 
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November 22, 2022
Journal Article
Title

Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation

Abstract
Analyzing vibration data using deep neural networks is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of the explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring. Thus, the three XAI algorithms GradCAM, LRP and LIME with a modified perturbation strategy are applied to classifications based on the Fourier transform as well as the order analysis of the vibration signal. The following visualization as frequency-RPM maps and order-RPM maps allows for an effective assessment of saliency values for variable periodicity of the data, which translates to a varying rotation speed of a real-world machine. To compare the explanatory power of the XAI methods, investigations are first carried out with a synthetic data set with known class-specific characteristics. Both a visual and a quantitative analysis of the resulting saliency maps are presented. Then, a real-world data set for vibration-based imbalance classification on an electric motor, which runs at a broad range of rotation speeds, is used. The results indicate that the investigated algorithms are each partially successful in providing sample-specific saliency maps which highlight class-specific features and omit features which are not relevant for classification.
Author(s)
Mey, Oliver  orcid-logo
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Neufeld, Deniz
Cognitive Systems Group, University of Bamberg
Journal
Sensors. Online journal  
Open Access
DOI
10.3390/s22239037
Additional full text version
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Language
English
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Keyword(s)
  • condition monitoring

  • explainable AI

  • fault detection

  • machine learning

  • order analysis

  • vibration analysis

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