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An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis.

Denis Leite1,2, Aldonso Martins2,3, Diego Rativa2

  • 1Mekatronik I.C. Automacao Ltda, R. Itapeva, 43a-Imbiribeira, Recife 51180-320, Brazil.

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Summary
This summary is machine-generated.

This study introduces an Automated Machine Learning (AutoML) system for real-time fault detection and diagnosis (RT-FDD) using common industrial data. The novel approach automates ML processes, enabling non-experts to deploy it for enhanced machine monitoring.

Keywords:
discrete-event systemsfault detectionfault diagnosisintelligent manufacturing systemsmachine learningsmart manufacturing

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Area of Science:

  • Industrial Automation
  • Machine Learning
  • Fault Diagnosis

Background:

  • Real-time fault detection and diagnosis (RT-FDD) is critical for industrial automation.
  • Existing methods often require expert intervention and specialized data.
  • Cyclic sequential machines present unique challenges due to combined discrete and continuous variables.

Purpose of the Study:

  • To present a novel Automated Machine Learning (AutoML) approach for RT-FDD.
  • To develop a system deployable by non-machine learning experts.
  • To improve fault detection performance in industrial automation systems.

Main Methods:

  • Developed an AutoML approach integrating discrete timed events and continuous variables.
  • Utilized commonly available industrial automation data.
  • Automated all machine learning processes without human intervention.
  • Incorporated analysis of cyclic sequential machine behavior.

Main Results:

  • Achieved high fault detection performance in two case studies using a 3D machine simulation system.
  • Demonstrated enhancement in RT-FDD performance with mean F1 Scores of 85% and 100%.
  • Analyzed model sensitivity to the number of faulty samples, crucial for rare event detection.

Conclusions:

  • The proposed AutoML approach offers an effective and accessible solution for RT-FDD.
  • The system successfully handles complex machine behaviors and data types.
  • This method enhances industrial automation reliability and maintainability.