Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis  

作　　者：Jinchuan Qian Li Jiang Zhihuan Song 

机构地区：[1]State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China [2]Shanghai Research Institute of Huawei Technology Co.,Ltd,Shanghai 200127,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2020年第3期764-775,共12页自动化学报（英文版）

基　　金：supported by the Key Project of National Natural Science Foundation of China(61933013);Ningbo 13th Five-year Marine Economic Innovation and Development Demonstration Project(NBH Y-2017-Z1)。

摘　　要：This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

关 键 词：Auto-encoder(AE) deep learning fault diagnosis LOCALLY LINEAR model nonlinear process reconstruction BASED contribution(RBC) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP277[自动化与计算机技术—控制科学与工程]