Outlier Detection for Process Monitoring in Industrial Cyber-Physical Systems

Abstract

The development of industrial cyber-physical system (ICPS) provides a tight connection between the digital model and the industrial physical plant, which enables to use the data-driven methods to reflect the state of process running in the real world. However, due to the noisy and harsh industrial environment, the collected data are often corrupted to some extent. If the corrupted data are not detected in time, the data-driven model will inevitably degenerate and induce a poor process monitoring performance. In addition, the nonlinear characteristics between process variables due to the high complexities in physical plant bring challenges to the data-driven methods. In this article, a robust kernel dictionary learning method, which can overcome the negative influence of outliers and simultaneously extracts the nonlinear characteristics of industrial process, is proposed to address the above problems in ICPS. Our extensive experiments demonstrate that the proposed method has achieved significantly better and stable performance to deal with outlier detection and process monitoring in ICPS. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In order to mitigate the impacts due to process noise and outliers, a robust kernel dictionary learning method is proposed to improve the accuracy and stability of the process monitoring of industrial cyber-physical systems. This method considers the process noise, sparse outlier, as well as the nonlinear characteristic of industrial systems for improving the accuracy and stability of monitoring. Compared with many state-of-the-art methods, the proposed method can detect the outliers in the training dataset adaptively, which is more applicable to the real industrial system.