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- Title
MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes.
- Authors
Wu, Junzhou; Zhang, Mei; Chen, Lingxiao
- Abstract
Incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of mixed kernel principal components analysis and dynamic canonical correlation analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient faults in nonlinear and dynamic systems. Meanwhile, fault identification experiments were conducted, validating the high accuracy of the fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.
- Subjects
NONLINEAR dynamical systems; CANONICAL correlation (Statistics); MANUFACTURING processes; PRINCIPAL components analysis; INDUSTRIALISM; FAULT diagnosis; STATISTICAL correlation
- Publication
Processes, 2023, Vol 11, Issue 10, p2927
- ISSN
2227-9717
- Publication type
Article
- DOI
10.3390/pr11102927