A Novel Adaptive SFA-LII Based Fault Detection Method for Nonstationary Processes

Multivariate statistical process monitoring (MSPM) methods have been widely used in industrial processes. Due to equipment aging, load changing and unknown disturbances, actual industrial processes tend to exhibit nonstationary characteristics. However, based on stationarity assumptions, traditional MSPM methods are unable to extract nonstationary features and accurately identify faults. In this paper, a novel adaptive fault detection method is proposed for fault detection of industrial processes mixed with stationary and nonstationary variations. First, nonstationary variables are selected by the unit root test. Second, the stationary residuals of the chosen variables are derived by Johansen cointegration analysis and form a new combination matrix together with the original stationary variables. Afterward, a slow feature analysis (SFA) monitoring model is established to realize feature level fusion, and the local information increment (LII) average is introduced as the monitoring statistic. Finally, considering that LII is dynamically updated and has the strongest correlation with the latest data, dynamic control limits are set based on the fuzzy membership function. For nonstationary processes, the proposed method demonstrates significant superiority in fault detection with higher fault detection rate and lower computational complexity. The effectiveness of the proposed method is validated by the Tennessee Eastman process and electric servo mechanisms.