A Transfer-Learning-Based Fault Detection Approach for Nonlinear Industrial Processes Under Unusual Operating Conditions

This article focuses on fault detection for nonlinear industrial processes with multiple operating conditions, in which transfer learning is used to deal with the limited training data issue for unusual operating conditions. To this end, the Tucker decomposition is first implemented to deliver the Gaussian kernel of the nonlinear processes with multiple operation conditions. Then, transfer learning is carried out based on correlation analysis to achieve fault detection for the target process. It is noted that the traditional statistic will lead to false alarms due to the switching of the operating conditions. To deal with this issue, a stationary statistic is investigated based on co-integration analysis. Finally, by transferring the fault detection systems from multiple operating conditions to unusual operating conditions based on extended manifold regularization, fault detection for unusual operating condition can be achieved with both the traditional statistics and the stationary statistic. The experimental result demonstrates the efficiency of the proposed fault detection method for the wastewater treatment process.