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Related Experiment Video

Updated: Aug 17, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical

Oliver Mey1, Deniz Neufeld2

  • 1Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, 01187 Dresden, Germany.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary

Explainable AI (XAI) methods enhance deep learning for machinery diagnostics. These techniques, including GradCAM, LRP, and LIME, improve the interpretability of vibration analysis for early fault detection.

Keywords:
condition monitoringexplainable AIfault detectionmachine learningorder analysisvibration analysis

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Deep neural networks (DNNs) excel at detecting machinery damage from vibration data.
  • The 'black-box' nature of DNNs hinders understanding the reasons behind damage classifications.
  • Interpreting these classifications is crucial for effective condition monitoring.

Purpose of the Study:

  • To apply explainable AI (XAI) algorithms to convolutional neural networks (CNNs) for vibration-based condition monitoring.
  • To enhance the interpretability of DNNs used in machinery diagnostics.
  • To assess the effectiveness of XAI in identifying damage-related features in vibration signals.

Main Methods:

  • Applied three XAI algorithms: GradCAM, LRP, and LIME, with a modified perturbation strategy.
  • Utilized classifications based on Fourier transform and order analysis of vibration signals.
  • Visualized results using frequency-RPM and order-RPM maps for variable periodicity analysis.

Main Results:

  • XAI methods generated sample-specific saliency maps highlighting relevant features for classification.
  • Visual and quantitative analyses confirmed the ability of XAI to identify class-specific characteristics.
  • The algorithms successfully omitted irrelevant features, improving classification interpretability.

Conclusions:

  • XAI algorithms offer valuable insights into DNN-based vibration analysis for machinery diagnostics.
  • The developed visualization techniques effectively assess saliency for varying machine speeds.
  • XAI significantly improves the comprehension of vibration-based condition monitoring systems.