Extraction of Reduced Fault Subspace Based on KDICA and Its Application in Fault Diagnosis

被引：0

作者：

Kong, Xiangyu ^{[1
]}

Yang, Zhiyan ^{[1
]}

Luo, Jiayu ^{[1
]}

Li, Hongzeng ^{[1
]}

Yang, Xi ^{[2
]}

机构：

[1] High-Tech Institute of Xi'an, Xi'an, Shaanxi,710025, China

[2] Beijing Institute of Precision Mechatronics and Controls, Beijing,100076, China

来源：

IEEE Transactions on Instrumentation and Measurement | 2022年 / 71卷

基金：

中国国家自然科学基金;

关键词：

Fault detection - Independent component analysis - Process monitoring - Failure analysis - Gaussian distribution - Learning algorithms - Gaussian noise (electronic);

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Independent component analysis (ICA) is a commonly used non-Gaussian process fault diagnosis method. A fault detection algorithm of kernel dynamic ICA (KDICA) has been proposed for the non-Gaussian process with dynamic and nonlinear characteristics. However, a lack of studies tackling the fault reconstruction and fault diagnosis algorithm exists. Hence, a fault reconstruction model based on KDICA is proposed in this article. In this model, a reduced fault subspace extraction method is proposed. It consists in dividing the fault subspace into the kernel dynamic independent component reduced fault subspace and the residual reduced fault subspace (RRFS). Based on the RRFS, a fault diagnosis approach is designed for online process monitoring. Using the proposed method, the computational complexity can be efficiently reduced and the specific fault type can be accurately identified. The Tennessee-Eastman process is used to verify the feasibility and efficiency of the proposed method and its fault diagnosis application. © 1963-2012 IEEE.

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