Automated feature learning for nonlinear process monitoring - An approach using stacked denoising autoencoder and k-nearest neighbor rule

Modern industrial processes have become increasingly complicated, consequently, the nonlinearity of data collected from these systems continues to increase. However, the feature extraction methods of existing process monitoring are not capable of extracting crucial features from these highly nonlinear data, which affects the performance of monitoring. In this paper, a novel nonlinear process monitoring method based on stacked denoising autoencoder (SDAE) and k-nearest neighbor (kNN) rule is proposed. Specifically, stacked denoising autoencoder is utilized to model the nonlinear process data and automatically extract crucial features. The original nonlinear space is then mapped to the feature space and the residual space via SDAE. Two new statistics in the above spaces are constructed by introducing the kNN rule with their corresponding control limits determined by kernel density estimation. Case studies on a nonlinear numerical system and the Tennessee Eastman benchmark process verify the effectiveness of the proposed method.