Abstract
Closed-loop data are widely encountered in modern industrial systems, which require special data analytics to gain insight for system monitoring. A closed-loop dynamic latent analysis scheme named closed-loop DiCCA (CL-DiCCA) is proposed in this article. Bidirectional dynamic latent variable relationships are proposed with a new objective to extract the closed-loop dynamic latent structure. An iterative algorithm is proposed to solve the constructed optimization problem for closed-loop processes. Four statistically independent residuals are generated, which monitor the dynamic and static variations of the process data. A process monitoring logic with the CL-DiCCA model is established, which offers further separation of faults into output-relevant and output-irrelevant ones. A numerical simulation and a case study on the thruster system of the Jiaolong deep-sea submersible are provided to illustrate the effectiveness of the proposed method.
Original language | English |
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Pages (from-to) | 1-11 |
Number of pages | 11 |
Journal | IEEE Transactions on Industrial Electronics |
Early online date | 23 Oct 2023 |
DOIs | |
Publication status | E-pub ahead of print - 23 Oct 2023 |
Bibliographical note
Publisher Copyright:IEEE
Keywords
- Bidirectional latent dynamic models
- closed-loop process monitoring
- Correlation
- Data models
- dynamic latent variable (DLV) analysis
- Heuristic algorithms
- Loading
- Predictive models
- Principal component analysis
- Process monitoring