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- Title
Gap-MK-DCCA-Based Intelligent Fault Diagnosis for Nonlinear Dynamic Systems.
- Authors
Wu, Junzhou; Zhang, Mei; Chen, Lingxiao
- Abstract
In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the T 2 statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection time compared to the comparative methods, demonstrating the industrial value and academic significance of the method.
- Subjects
NONLINEAR dynamical systems; FAULT diagnosis; STATISTICAL correlation; SPEED of sound; FALSE alarms; COMPARATIVE method; DISTRIBUTED parameter systems
- Publication
Processes, 2024, Vol 12, Issue 2, p388
- ISSN
2227-9717
- Publication type
Article
- DOI
10.3390/pr12020388