Journal
IET CONTROL THEORY AND APPLICATIONS
Volume -, Issue -, Pages -Publisher
WILEY
DOI: 10.1049/cth2.12550
Keywords
distributed algorithms; fault diagnosis; fault location
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This paper explores data-driven distributed fault diagnosis for large-scale systems using sensor networks. It introduces a distributed fault detection scheme based on correlation analysis to enhance fault detection performance by reducing the impact of noise-induced uncertainty. The method focuses on implementing the correlation of coupled nodes to minimize the covariance of the residual signal in a distributed manner. Additionally, a fault localization approach is developed to identify faults by measuring and comparing the degree of abnormality.
This paper studies data-driven distributed fault diagnosis for large-scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection performance by minimizing the impact of noise-induced uncertainty. The core of the method is to implement the correlation of the coupled nodes to reduce the covariance of the residual signal in a distributed manner. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality. A case study on Tennessee Eastman process is given in the end to demonstrate the proposed approach. This paper studies data-driven distributed fault diagnosis for large-scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to mitigate the impact of noise-induced uncertainty on fault detection performance. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality.image
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