Process fault detection based on dynamic kernel slow feature analysis

作者：Zhang, Ni; Tian, Xuemin^*; Cai, Lianfang; Deng, Xiaogang

来源：Computers & Electrical Engineering, 2015, 41: 9-17.

DOI：10.1016/j.compeleceng.2014.11.003

• Fault detection
• Slow feature analysis
• Kernel principal component analysis
• Nonlinear dynamic process

A fault detection method based on dynamic kernel slow feature analysis (DKSFA) is presented in the paper. SFA is a new feature extraction technology which can find a group of slowly varying feature outputs from the high-dimensional inputs. In order to analyze the nonlinear dynamic characteristics of the process data, DKSFA is presented which applies the augmented matrix to consider the dynamic characteristic and uses kernel slow feature analysis (KSFA) to extract the nonlinear slow features hidden in the observed data. For the purpose of fault detection, the D monitoring statistic index is built based on DKSFA model and its confidence limit is computed by kernel density estimation. Simulations on a nonlinear system and Tennessee Eastman (TE) benchmark process show that the proposed method has a better fault detection performance compared with the conventional (kernel principal component analysis) KPCA-based method.

• 出版日期2015-1
• 单位中国石油大学（北京）; 潍坊学院