Fault diagnosis in a wastewater treatment plant using dynamic Independent Component Analysis

Abstract

In this paper Dynamic Independent Component Analysis (DICA) is used for detecting and diagnosing faults in a wastewater treatment plant. ICA is a method in which the goal is to decompose a set of multivariate data into a base of statistically independent components without loss of information. The DICA monitoring method applies ICA to the augmenting matrix with time-lagged variables. The basic idea of this approach is to use DICA to extract the essential independent components that drive the process in each situation, i.e, in normal operation and in different faulty situations, and to combine them with process monitoring techniques as I2, I2e and SPE for fault detection and diagnosis. The considered charts are compared with their minimum thresholds, with and without faults. The only one that does not violate its threshold tells us the actual system situation, i.e., identifies the fault. This method is applied to the simulation of a benchmark of the biological wastewater treatment process, which is characterized by a variety of fault sources with non-Gaussian characteristics. The simulation results clearly show the advantages of DICA monitoring in comparison with DPCA monitoring.