Nonlinear Multimode Industrial Process Fault Detection Using Modified Kernel Principal Component Analysis

Kernel principal component analysis (KPCA) has been a state-of-the-art nonlinear process monitoring method. However, KPCA assumes the single operation mode while the real industrial processes often run under multiple operation conditions. In order to monitor the nonlinear multimode processes effectively, this paper proposes a modified KPCA method assisted by the local statistical analysis, referred to as local statistics KPCA (LSKPCA). In the proposed method, two kinds of strategies, including local probability density estimation and statistics pattern analysis, are integrated to improve the traditional KPCA method. To handle the multimode characteristic of industrial processes, local probability density estimation is developed to transform the monitored variables into their probability density values, which follow the unimodal data distribution. For further extracting the statistical information among the process data, statistics pattern analysis technique is applied to capture various orders of statistics, including one-order, second-order, and high-order ones, which constitute the statistics pattern matrix of the monitored data. Furthermore, KPCA modeling is performed on the statistics pattern matrix. The simulations on one numerical example and the continuous stirred tank reactor system demonstrate that the proposed LSKPCA method has the superior fault detection performance compared with the conventional KPCA method.