Early identification of process deviation based on convolutional neural network

doi:10.1016/j.cjche.2022.07.034

摘要/Abstract

摘要： A novel process monitoring method based on convolutional neural network (CNN) is proposed and applied to detect faults in industrial process. By utilizing the CNN algorithm, cross-correlation and autocorrelation among variables are captured to establish a prediction model for each process variable to approximate the first-principle of physical/chemical relationships among different variables under normal operating conditions. When the process is operated under pre-set operating conditions, prediction residuals can be assumed as noise if a proper model is employed. Once process faults occur, the residuals will increase due to the changes of correlation among variables. A principal component analysis (PCA) model based on the residuals is established to realize process monitoring. By monitoring the changes in main feature of prediction residuals, the faults can be promptly detected. Case studies on a numerical nonlinear example and data from two industrial processes are presented to validate the performance of process monitoring based on CNN.

关键词: Process monitoring, Residual, Principal component analysis, Process systems, Systems engineering

Abstract: A novel process monitoring method based on convolutional neural network (CNN) is proposed and applied to detect faults in industrial process. By utilizing the CNN algorithm, cross-correlation and autocorrelation among variables are captured to establish a prediction model for each process variable to approximate the first-principle of physical/chemical relationships among different variables under normal operating conditions. When the process is operated under pre-set operating conditions, prediction residuals can be assumed as noise if a proper model is employed. Once process faults occur, the residuals will increase due to the changes of correlation among variables. A principal component analysis (PCA) model based on the residuals is established to realize process monitoring. By monitoring the changes in main feature of prediction residuals, the faults can be promptly detected. Case studies on a numerical nonlinear example and data from two industrial processes are presented to validate the performance of process monitoring based on CNN.

Key words: Process monitoring, Residual, Principal component analysis, Process systems, Systems engineering

Fangyuan Ma, Cheng Ji, Jingde Wang, Wei Sun. Early identification of process deviation based on convolutional neural network[J]. 中国化学工程学报, 2023, 56(4): 104-118.

Fangyuan Ma, Cheng Ji, Jingde Wang, Wei Sun. Early identification of process deviation based on convolutional neural network[J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 104-118.

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