A Novel Deep DPCA-SVM Method for Fault Detection in Industrial Processes
- Resource Type
- Conference
- Authors
- Zhang, Jian; Zou, Jianxiao; Zhang, Jiyang; Tao, Qian; Gui, Xingtai; Xu, Hongbing; Fan, Shicai
- Source
- 2019 IEEE 58th Conference on Decision and Control (CDC) Decision and Control (CDC), 2019 IEEE 58th Conference on. :2916-2921 Dec, 2019
- Subject
- Aerospace
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Matrix decomposition
Fault detection
Support vector machines
Principal component analysis
Feature extraction
Heuristic algorithms
Monitoring
- Language
- ISSN
- 2576-2370
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.