Nonlocal, local and global preserving stacked autoencoder based fault detection method for nonlinear process monitoring

Abstract

In recent years, the application of deep learning methods has improved the ability of nonlinear feature extraction in industrial processes. However, most of the deep learning methods cannot consider the manifold structure-related information (i.e., nonlocal, local and global manifold structure) of the data. To extract the vital structure-related features, a novel local, nonlocal and global preserving stacked autoencoder (NLGPSAE) for nonlinear process monitoring is proposed. NLGPSAE has an ability to extract crucial structure-related features by introducing a new regularized objective function with local, nonlocal and global structural information. For the preservation of local structure, NLGPSAE can preserve the original neighbor data points to be neighbor data points in the reconstructed space; for the preservation of nonlocal structures, NLGPSAE projects the nonlocal data points to be far apart in the reconstructed space; for the preservation of the global structure, the distance relationship between the original data points and the center of the data points is preserved in the reconstructed space. Two statistics Hotelling's T-squared (T2) and squared prediction error (SPE) based on features extracted by NLGPSAE are established for fault detection. The effectiveness of the proposed algorithm is verified in a complex numerical process and Tennessee Eastman process.