Dynamic monitoring method for multiscale fault detection and diagnosis in MSPC

Abstract

A dynamic monitoring method for multiscale fault detection and diagnosis (MFDD) in the wastewater treatment process (WWTP) is proposed. This method is based on dynamic principal component analysis (DPCA) and the D statistic and the monitoring, of individual eigenvalues of generic dissimilarity measure (GDM). DPCA method, where the,original measurements are lagged, describes a cause and effect of the process. Then the GDM of the, DPCA score value, called the D statistic, is used to detect faults - and events, of the:current operating condition. Additionally, monitoring of individual eigenvalues enables the diagnosis of diverse kinds of fault and disturbance sources. The DPCA and the dynamic GDM, of the MFDD can be used as a remedy to the problem of analyzing nonstationary processes. The proposed method was applied to fault monitoring and isolation in simulation benchmark data and real. plant data. The simulation results clearly show that the method effectively detects faults in a dynamic, multivariate, and multiscale process. Moreover, the proposed approach knot only detects faults but also isolates the sources of them to some degree.