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Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation

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Summary
This summary is machine-generated.

Industrial collaborative robots (cobots) face challenges in fault diagnosis due to concept drift. This study introduces an unsupervised anomaly detection method that distinguishes concept drift from failures and adapts to new conditions.

Keywords:
anomaly detectioncollaborative roboticsconcept drift adaptationfault detectionmachine learning

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Industrial Engineering

Background:

  • Industrial collaborative robots (cobots) are versatile, operating in dynamic environments and flexible manufacturing.
  • Traditional fault diagnosis methods struggle with cobots due to varying working conditions, leading to concept drift.
  • Concept drift, a change in data distribution, complicates anomaly detection in nonstationary systems.

Purpose of the Study:

  • To propose an unsupervised anomaly detection (UAD) method for cobots that can operate under concept drift.
  • To differentiate between concept drift (changes in working conditions) and system degradation (failures).
  • To enable model adaptation to new working conditions upon detecting concept drift.

Main Methods:

  • Development of an unsupervised anomaly detection (UAD) algorithm.
  • Implementation of a mechanism to distinguish between concept drift and system failures.
  • Integration of an adaptive component for model retraining upon concept drift detection.

Main Results:

  • The proposed UAD method effectively identifies data changes caused by concept drift.
  • The method successfully distinguishes between concept drift and actual system failures.
  • The adaptive capability allows the model to adjust to new operational conditions, preventing misinterpretation.

Conclusions:

  • The developed UAD method is suitable for monitoring industrial cobots in dynamic environments with concept drift.
  • The approach enhances the reliability of fault diagnosis by differentiating operational changes from failures.
  • Proof of concept demonstrates the method's efficacy on an industrial collaborative robot.