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Multi-Source Diagnosis of Bearing Faults Using Interpretable Boosted Trees.

Miguel Fernández-Temprano1, Manuel Astorgano-Antón1, Óscar Duque-Pérez2

  • 1Department of Statistics and Operational Research, Escuela de Ingenierías Industriales, Universidad de Valladolid, 47011 Valladolid, Spain.

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|March 14, 2026
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Summary
This summary is machine-generated.

Early detection of induction motor faults, especially bearing issues, is crucial for industry. This study uses explainable AI to identify the best sensor data and domains for accurate fault diagnosis, improving industrial reliability.

Keywords:
SHAP valuesboosted treesdata fusiondiagnostic expert systemsinduction motorsmonitoring

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Industrial Maintenance

Background:

  • Induction motors are critical in industrial applications, and early fault detection prevents significant financial losses.
  • Bearing faults are the most common issue in induction motors, necessitating reliable diagnostic techniques.
  • Traditional diagnosis relies on vibration analysis, but supply current and sound are also explored, in both time and frequency domains.

Purpose of the Study:

  • To employ explainable artificial intelligence (XAI) for identifying optimal sensor data and domains for induction motor fault diagnosis.
  • To quantify the diagnostic improvements achievable through multisensor data fusion.
  • To objectively compare different diagnostic approaches using a rigorous model selection process.

Main Methods:

  • Utilized explainable artificial intelligence (XAI) techniques, including SHAP values, for interpreting diagnostic models.
  • Applied boosting techniques for precise fault diagnosis.
  • Implemented a structured model selection procedure for objective comparison of diagnostic variables and domains.

Main Results:

  • Identified specific sensor variables and domains that significantly contribute to accurate induction motor fault diagnosis.
  • Demonstrated the diagnostic benefits of integrating data from multiple sensors (multisensor data fusion).
  • Achieved highly precise diagnoses through the application of boosting techniques and XAI interpretation.

Conclusions:

  • Explainable AI provides valuable insights into the most effective data sources and domains for induction motor fault diagnosis.
  • Multisensor data fusion enhances diagnostic accuracy, leading to improved industrial reliability.
  • SHAP value interpretation of boosting models offers a transparent and effective approach to understanding complex diagnostic decisions.