Switched Fault Diagnosis Approach for Industrial Processes based on Hidden Markov Model

12th European Workshop on Advanced Control and Diagnosis

Wang, Lin^1 ; Yang, Chunjie^1 ; Sun, Youxian^1 ; Pan, Yijun^1 ; An, Ruqiao^1

State Key Laboratory of Industrial Control Technology, Institute of Industrial Processes Control, Zhejiang University, Hangzhou, Zhejiang, China^1

关键词: Data characteristics;    Extraction procedure;    Fault diagnosis method;    Feature extraction methods;    Independent component analyses (ICA);    Kernel principal component analyses (KPCA);    Non-linear relationships;    Nonlinearity measures;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012047/pdf
DOI  :  10.1088/1742-6596/659/1/012047

来源: IOP

【 摘 要 】

Traditional fault diagnosis methods based on hidden Markov model (HMM) use a unified method for feature extraction, such as principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA). However, every method has its own limitations. For example, PCA cannot extract nonlinear relationships among process variables. So it is inappropriate to extract all features of variables by only one method, especially when data characteristics are very complex. This article proposes a switched feature extraction procedure using PCA and KPCA based on nonlinearity measure. By the proposed method, we are able to choose the most suitable feature extraction method, which could improve the accuracy of fault diagnosis. A simulation from the Tennessee Eastman (TE) process demonstrates that the proposed approach is superior to the traditional one based on HMM and could achieve more accurate classification of various process faults.