An Environmentally Adaptive and Contrastive Representation Learning Method for Condition Monitoring of Industrial Assets

Shilin Sun, Tianyang Wang, Hongxing Yang, Fulei Chu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Condition monitoring of assets is significant to the efficiency and reliability of industrial automation systems. However, the accuracy of condition monitoring results is easily impaired by variational environments and volatile operations, especially for complex automation systems. In this article, an environmentally adaptive and contrastive representation learning method is proposed to address the problem. To suppress the unexpected effects of environmental variations on operating data, a regression model between the operational and environmental variables is developed. The variable regression adjustment is achieved by solving a penalized optimization problem based on spline functions, and the solution is explicitly derived. Then, negative samples and pseudo labels are generated based on the designed pattern of data augmentation, and valid data representations for asset condition monitoring can be obtained by contrastive learning. Moreover, the reference statue of healthy assets is established by kernel density estimation, and control charts are employed for online monitoring with alarm thresholds. Taking wind turbine blades as examples, the remarkable performance of the developed method is demonstrated with real-world measurements from wind farms. Furthermore, comparative analysis with benchmark approaches and ablation study are conducted to reveal the superiority and effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusPublished - Oct 2022

Keywords

  • Artificial intelligence
  • Automation
  • Condition monitoring
  • condition monitoring
  • contrastive learning
  • Monitoring
  • representation learning
  • Representation learning
  • Splines (mathematics)
  • Task analysis
  • Training

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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