A Novel Deep DPCA-SVM Method for Fault Detection in Industrial Processes

Fault detection is an important step to ensure safe and reliable production in industrial processes. Data-driven technology is one of the most widely studied fault detection methods. This paper proposed a data-driven fault detection method named deep dynamic Principal Component Analysis-Support Vector Machine (Deep DPCA-SVM) for industrial processes. By constructing a multi-layer DPCA structure for robust feature extraction, a fault detection model with high precision could be retrieved based on the SVM classifier. The proposed Deep DPCA-SVM method was applied to the Tennessee Eastman (TE) process, and its superior performances indicated that our proposed method could extract the more efficient features for the fault detection.