Support-vector-data-description Process Monitoring Algorithm Based on Neighborhood Preserving Embedding and Its Application

To detect faults in nonlinear and multimode batch processes, we propose a fault detection strategy that uses support vector data description based on neighborhood preserving embedding (NPE-SVDD). First, we project the raw data into the feature space using the NPE algorithm. Then, we build the SVDD model in the feature space and calculate the center of the sphere O and the radius R of the hyper sphere. For a test sample, we calculate the distance D from it to the center of the sphere, and compare the magnitudes of D and R to determine the sample state. After determining the sample state, we use the distance contribution mapping method to determine the location of the fault variable. The NPE algorithm can preserve the local information of the raw data. NPE-SVDD eliminates the limitations associated with data that obey a Gaussian distribution and improves the fault detection rate by replacing the T² and SPE statistics of the original NPE algorithm with SVDD. We use numerical simulation and semiconductor-etching-process simulation to compare the experimental results with those obtained by principal component analysis, NPE, SVDD and other methods to verify the effectiveness of the NPE-SVDD method.