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Related Experiment Video

Updated: Oct 7, 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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Sensor-Based Predictive Maintenance with Reduction of False Alarms-A Case Study in Heavy Industry.

Marek Hermansa1, Michał Kozielski1, Marcin Michalak1

  • 1Department of Computer Networks and Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland.

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

A new predictive maintenance method identifies undesirable events using outlier detection and eXplainable Artificial Intelligence (XAI). This approach significantly reduces false alarms, improving system reliability in industrial settings with limited historical data.

Keywords:
XAIfalse positive reductionmultidimesional timeseries analysisoutlier detectionpredictive maintenancevibration measurements

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Area of Science:

  • Industrial Engineering
  • Data Science
  • Machine Learning

Background:

  • Undesirable events in industrial settings are often poorly represented in historical data, hindering traditional predictive maintenance.
  • Existing systems can overwhelm operators with false alarms, especially in cold start conditions.

Purpose of the Study:

  • To propose a novel predictive maintenance method for identifying undesirable events in industrial machinery.
  • To address the challenge of limited historical data and reduce false positive alarms in real-time monitoring.

Main Methods:

  • Analysis of vibration and temperature data from wireless sensors in crushers and gantries.
  • Application of outlier identification methods to multidimensional feature vectors.
  • Development of a false positive alarm reduction methodology incorporating data adaptation, dispatcher interaction, and eXplainable Artificial Intelligence (XAI).

Main Results:

  • The proposed method achieved an average reduction of 90.25% in false alarms compared to stand-alone outlier detection.
  • The methodology proved effective in cold start scenarios and was successfully implemented in a real industrial facility.

Conclusions:

  • The developed predictive maintenance method offers a robust solution for identifying undesirable events, particularly in systems with limited historical data.
  • The reduction in false alarms enhances user trust and system adoption, crucial for effective industrial monitoring.