A neural network-based control chart for monitoring and interpreting autocorrelated multivariate processes using layer-wise relevance propagation

Recent advances in sensing and information technology are enabling multivariate sensory data from industrial equipment to be collected at a high sampling frequency. The resulting data streams often exhibit strong autocorrelation. In this study, we propose a neural network-based residual control chart to monitor the autocorrelated multivariate processes for real-time detection of abnormal equipment performance. Furthermore, we propose an interpretation method, based on layer-wise relevance propagation (LRP), to identify the responsible variable when an out-of-control condition is detected by the control chart. We compare the proposed techniques with several existing methods. Numerical studies and a real-data application demonstrate that the proposed method has good monitoring performance and can provide effective interpretation results. The proposed methods can be applied broadly for system condition monitoring and change detection in a variety of industrial systems applications.