A Joint Stacked Autoencoder Approach with Silhouette Information for Industrial Fault Detection

Abstract

Due to the growing complexity of industrial processes, it is no longer adequate to perform precise fault detection based solely on the global information of process data. In this study, a silhouette stacked autoencoder (SiSAE) model is constructed for process data by considering both global/local information and silhouette information to depict the link between local/cross-local. Three components comprise the SiSAE model: hierarchical clustering, silhouette loss, and the joint stacked autoencoder (SAE). Hierarchical clustering is used to partition raw data into many blocks, which clarifies the information’s characteristics. To account for silhouette information between data, a silhouette loss function is constructed by raising the inner block’s data distance and decreasing the distance of the cross-center block. Each data block has a properly sized SAE model and is jointly trained via silhouette loss to extract features from all available data. Using the Tennessee Eastman (TE) benchmark and semiconductor industrial process data, the proposed method is validated. Comparative tests on the TE benchmark indicate that the average rate of fault identification increases from 75.8% to 83%, while the average rate of false detection drops from 4.6% to 3.9%.