Details

Autor(en) / Beteiligte

• Li, Zhennan
• Li, Linlin
• Ding, Steven X.

Titel

A data‐driven distributed fault diagnosis scheme for large‐scale systems based on correlation analysis

Ist Teil von

• IET control theory & applications, 2024-01, Vol.18 (2), p.201-212

Ort / Verlag

Wiley

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Wiley-Blackwell Full Collection

Beschreibungen/Notizen

• 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.