Fault diagnosis of dynamic processes with reconstruction and magnitude profile estimation for an industrial application

Qiang LIU*, Bo SONG, Xuecheng DING, S. Joe QIN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

13 Citations (Scopus)

Abstract

Fault diagnosis is essential for troubleshooting and maintenance of industrial processes that operate dynamically. Traditional reconstruction-based fault diagnosis methods, however, are mostly developed for static processes and are ineffective for faults with similar directions. In this paper, a new hierarchical fault diagnosis strategy that incorporates reconstruction and dynamic time warping is proposed for the feeding anomaly diagnosis of an industrial cone crusher. A novel fault-magnitude-estimation method for dynamic processes is proposed based on the dynamic relations captured by dynamic latent variable (DLV) predictions. A combined index is developed based on the prediction residuals which exclude normal and predictable variations to improve sensitivity to faults. Fault-magnitude-estimation-based dynamic time warping is proposed to evaluate the shape similarity of faults in order to further isolate the fault candidates with similar directions. The reconstructed magnitude is utilized to extract shape features of the faults. The advantages are demonstrated using a Monte-Carlo simulation example of a dynamic process. Finally, the proposed method is applied successfully to diagnose feeding anomalies of an industrial cone crusher.
Original languageEnglish
Article number105008
JournalControl Engineering Practice
Volume121
Early online date11 Jan 2022
DOIs
Publication statusPublished - Apr 2022
Externally publishedYes

Bibliographical note

This work was supported by the National Key Research and Development Project (2020YFB1710003), National Natural Science Foundation of China (61991401, U20A20189, 62161160338), LiaoNing Revitalization Talents Program (XLYC1907049), and the Fundamental Research Funds for the Central Universities (N180802004).

Keywords

  • Cone crusher
  • Dynamic latent variable
  • Dynamic process
  • Dynamic time warping
  • Fault diagnosis

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