Process Monitoring Based on Robust Slow Feature Analysis

A process monitoring method based on robust slow feature analysis (RSFA) is presented in the paper. Slow feature analysis (SFA) is a new feature extraction technology which can find a group of slowly varying feature outputs from the high-dimensional inputs. In order to improve the robustness of SFA, RSFA is presented which applies locality preserving projection (LPP) to define the energy density function and set a reasonable threshold value to remove outliers mixed in the normal process data. This paper uses three spline interpolation method to interpolate the missing value caused by removing the outliers to maintain the integrity of the time series. For the purpose of fault detection, the D monitoring statistic index is adopted and its confidence limit is computed by kernel density estimation. Simulation on Tennessee Eastman (TE) benchmark process show that the proposed method has a better fault detection performance compared with the conventional SFA-based method.