Dynamic higher-order cumulants analysis for state monitoring based on a novel lag selection

Abstract

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 state monitoring purpose. Although HCA has a strong capability for state 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 step of dynamic state monitoring methods is 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 simple selection method may not 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 for each individual variable is proposed in this study. Hence, two dynamic higher-order cumulants analysis (DHCA) approaches are proposed for state monitoring, among which one is based on the conventional lag selection method and another is based on the new lag selection method proposed in this study. The two kinds of DHCA approaches are tested on the Tennessee Eastman process, and are demonstrated to be superior to all the compared methods.