Active features extracted by deep belief network for process monitoring. (January 2019) - Yu, Jianbo; Yan, Xuefeng
Abstract: Recently, based on the powerful capability of feature extraction, deep learning technique has been applied to the field of process monitoring, and usually, the researches utilize all the abstract features to establish the detection model and detect or classify the fault. However, whether all the extracted features are valid and beneficial for process monitoring have never been researched and discussed. If there are some features that are adverse for process monitoring, the detection performance of the model would be reduced once they are considered in the model, and utilized the features that are advantageous for process monitoring could ameliorate the performance of detection model. Motivated by this, a feasibility analysis on each feature captured by deep belief network for process monitoring is executed and the conception of active features (AFs) which have active expression for the occurrence of the fault is proposed. Based on AFs, utilized Euclidean metric to calculate the dissimilarity between the test sample and the training sample, and moving average technique is employed to reduce the effect of the burst noise in measurement variables on the result. Finally, the comparison of fault detection rate with other advanced methods on a numerical process and TE process demonstrate the feasibility and superiority of the proposed method, AF-DBN in this study. Highlights: Utilized the powerful feature extraction capability of deep learning to perform industrial process monitoring. Perform the analysis of all the features extracted by DBN and propose the conception of "activity degree" and "active features". Based on active features to perform the process monitoring and eliminate the adverse effects of inactive features on fault detection. Avoid the adverse influence on monitoring results of the burst noise in measurement variables. Different monitoring performance influenced by parameters of DBN are demonstrated in detail.