A data‐driven distributed fault detection scheme based on subspace identification technique for dynamic systems

With the aid of the subspace technique and the average consensus algorithm, the main objective of this article is to develop a data‐driven design of distributed fault detection for dynamic systems using the measurement in a complex sensor network. Specifically, the design process consists of two sta...

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Veröffentlicht in:International journal of robust and nonlinear control 2023-03, Vol.33 (5), p.3107-3128
Hauptverfasser: Cheng, Chao, Wang, Qiang, Nikitin, Yury, Liu, Chun, Zhou, Yang, Chen, Hongtian
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container_end_page 3128
container_issue 5
container_start_page 3107
container_title International journal of robust and nonlinear control
container_volume 33
creator Cheng, Chao
Wang, Qiang
Nikitin, Yury
Liu, Chun
Zhou, Yang
Chen, Hongtian
description With the aid of the subspace technique and the average consensus algorithm, the main objective of this article is to develop a data‐driven design of distributed fault detection for dynamic systems using the measurement in a complex sensor network. Specifically, the design process consists of two stages: distributed off‐line learning and distributed online fault detection. Among them, the distributed off‐line learning stage involves the average consensus algorithm and parameter identification by subspace technique. It is worth mentioning that, the distributed fault detection approach has the same performance as the centralized fault detection approach and avoids complex information exchange. In the end, a numerical simulation example and a case study of the three‐phase flow facility are illustrated to show that the proposed distributed approach can accomplish the fault detection task successfully.
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subjects Algorithms
average consensus
Data exchange
data‐driven designs
distributed fault detection
Dynamical systems
Fault detection
Machine learning
Parameter identification
sensor networks
subspace identification
Subspaces
title A data‐driven distributed fault detection scheme based on subspace identification technique for dynamic systems
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