An Unsupervised Fault Detection and Diagnosis With Distribution Dissimilarity and Lasso Penalty

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Wanke Yu
  • Chunhui Zhao
  • Biao Huang
  • Min Xie

Detail(s)

Original languageEnglish
Pages (from-to)767-779
Number of pages13
Journal / PublicationIEEE Transactions on Control Systems Technology
Volume32
Issue number4
Online published16 Nov 2023
Publication statusPublished - May 2024

Abstract

Unsupervised fault detection and diagnosis methods generally have the following shortcomings in their projection vectors: 1) they may not be specially designed to differentiate between normal and abnormal samples; 2) they may remain unchanged for different abnormal conditions; and 3) the key variables for the process anomalies may have not been effectively selected. In this study, a fault detection and diagnosis scheme sparse distribution dissimilarity analytics (SDDA) is proposed with a lasso penalty and distribution dissimilarity to solve these issues. The proposed method is formulated through a nonconvex optimization problem with a lasso penalty, which aims to maximize the distribution dissimilarity between different data sets. Then, the nonconvex optimization problem is recast to an iterative convex optimization problem using the minorization–maximization algorithm. After that, the constraint conditions are removed using the Karush–Kuhn–Tucker conditions for further simplification. Finally, the unconstraint optimization problem is solved through the proposed feasible gradient direction method. Based on the obtained sparse projection vectors, a fault detection model with both static deviation and dynamic fluctuation is developed. Since the statistics are designed using distribution dissimilarity, some abnormal conditions with small fault magnitudes can also be accurately detected. Besides, a reconstruction-based contribution (RBC) method is proposed for the statistics, and its diagnosability has been strictly demonstrated in theory. The detection and diagnosis performance of the proposed SDDA method is validated using a simulated process and a real industrial process. Experimental results illustrate the superiority of the proposed method to some commonly used methods. © 2023 IEEE.

Research Area(s)

  • Diagnosability analysis, distribution dissimilarity, Fault detection, fault detection and diagnosis, lasso penalty, Monitoring, nonconvex optimization, Optimization, Principal component analysis, Process control, Testing, Training data