A distributed alternating optimization approach to canonical correlation analysis based fault detection for dynamic
Chenyang Wang1, Zhenjin Zhao1, Linlin Li2
1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Abstract:
In this paper, a data-driven distributed alternating optimization approach to optimal fault detection is proposed for dynamic processes based on canonical correlation analysis (CCA). The focus of this method is to reduce the uncertainties caused by measurement noise using relevant information from the neighboring subsystems. Specifically, the average consensus algorithm is used in the alternating optimization algorithm to calculate the CCA parameters, thereby enabling each subsystem to update the parameters simultaneously. Then, a distributed residual generator can be constructed using the obtained CCA parameters for the fault detection purposes. Compared with the centralized methods, the communication cost between nodes is reduced and the computation efficiency is improved by the proposed distributed approach. Based on it, case studies on the hot rolling mill process and Tennessee Eastman process are used to demonstrate the proposed method.
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