Dynamic Higher-Order Cumulants Analysis For Process Monitoring Based On A Novel Lag Selection

Higher-order cumulants analysis (HCA) is an up-to-date method that utilizes higher-order cumulants rather than lower-order statistics (e.g., variances) to achieve the process monitoring purpose. Although HCA has a strong capability for process monitoring, it still exhibits many inadequacies for monitoring dynamic processes. Currently, there are various approaches (e.g., dynamic principle component analysis and dynamic independent component analysis) that are applicable to dynamic features. However, the key problem of dynamic statistical process monitoring methods is the determination of the time lags or the lag structure. Almost all the reported dynamic methods select a single number of time lags for all variables. This common selection method may not always be appropriate since it is generally not possible that all variables have the same lag structure. In order to address this issue, a new lag selection method is proposed in this study, which designs individual lag structure for each variable. Hence two dynamic HCA (DHCA) approaches are proposed for process monitoring, among which one is based on the conventional lag selection method and another is based on the novel lag selection method proposed in this study. The two proposedDHCA approaches are tested on the Tennessee Eastman process, and are demonstrated to be superior to all the compared methods.