Ensemble Monitoring Model Based on Multi-Subspace Partition of Deep Features

Abstract

Traditional deep neural network (DNN) based process monitoring methods only use the deep features of the last layer and residuals to achieve fault detection. However, the features in different hidden layers are different representations of the input data, which may be beneficial to process monitoring. Only using the deepest features for process monitoring will cause the problems of information loss and low monitoring performance. To obtain more useful information for fault detection, this paper considers the features in all hidden layers and proposed an ensemble monitoring model based on multi-subspace partition of deep features. Firstly, a DNN model is established based on the collected faultless data to obtain the features in all hidden layers and residuals. Secondly, a new feature matrix is constructed based on the retained deep features and residuals. Then, the multi-subspace partition of the new feature matrix is realized by combining correlation analysis and cluster analysis. Finally, the monitoring statistics that are established based on the features in each subspace are fused to realize process monitoring. The proposed method can not only reduce information loss but also enrich the fault-related information. The monitoring performance is verified through two benchmark processes and one actual industrial process.