Multimode process monitoring using adaptive auto‐associative kernel regression

Abstract

Complex modern industrial processes often exhibit multimodal characteristic because of different manufacturing strategies. Although the conventional auto‐associative kernel regression (AAKR) method is suitable for monitoring the nonlinear multimodal processes, different high Pearson correlations between variables from different modes reduce the fault detection accuracy of AAKR model. Moreover, within‐mode process data usually present the property of serial correlation, resulting in the auto‐correlation and cross‐correlation of variables simultaneously existing in the dynamic processes. In order to reduce the influence of correlation and improve the accuracy of fault detection based on AAKR, a novel multimode process monitoring method combining zero‐phase component analysis (ZCA) as a data preprocessing technique and traditional AAKR is proposed. To further enhance the detectability of the model to small disturbance, a modified statistic based on multivariate exponentially weighted moving average (MEWMA) is studied in this paper. The monitoring performance of the proposed approach is evaluated through a numerical example, the Tennessee Eastman (TE) process and the real Bisphenol‐A (BPA) production process.