Fault detection for chemical processes with outliers based on auto-associative kernel regression

The data driven fault detection models usually require that the training data must be measured under normal operating conditions. However, in the actual industrial processes, it is possible that the collected data set contains outliers even under normal working conditions. In this case, the control limits of the traditional method based on multivariate statistical analysis are often heavily influenced by the outliers, which results in a large number of missed failures. Therefore, in order to ensure that the monitoring model still has good performance even when the training data contains outliers, this paper proposed a fault detection method based on auto-associative kernel regression (AAKR). First, the training set is whitened on the basis of a robust whitening algorithm that minimizes β divergence to eliminate the influence of correlation between variables on sample similarity measurement. Then, AAKR reconstructs the validation data under normal working conditions to obtain the residuals and establish the correct detection index. In order to avoid the influence of outliers on the reconstructions of faulty test data, a truncation function is constructed to avoid the involvement of outliers similar to the faulty samples in reconstruction. All residual variables involved in Q statistic construction were weighted based on the exponentially weighted moving average (EWMA) to obtain the new monitoring statistic. The proposed method is applied to the Tennessee Eastman (TE) process to verify the effectiveness of the proposed fault detection algorithm. © 2023 South China University of Technology. All rights reserved.