Fault Detection and Monitoring Using Multiscale Principal Component Analysis at a Sewage Treatment Plant

Abstract

Safety, environmental regulations, the cost of maintenance and the operation of sewage treatment plants are some of the many reasons researchers have carried out countless research studies into fault detection and monitoring over the years. Conventional principal component analysis (PCA) in particular has been used in the field of fault detection, where the technique is able to separate useful information from multivariate data. However, conventional PCA can only be used on data that has a constant mean, which is rare in sewage treatment plants. Consequently, the success of combining wavelet and conventional PCA has attracted many researchers to apply it to fault detection where the wavelet is capable of separating data into several time scales. The separated data will be approximated to a constant mean. In addition, the conventional PCA only captures the correlation across the data, unlike multiscale PCA (MSPCA) which captures the correlation within the data and across the data. Therefore, in this work, MSPCA is introduced to improve the performance of PCA in fault detection. The objective of this paper is to reduce false alarms that exist in PCA fault detection and monitoring. Data from the Bunus sewage treatment plant (Bunus STP) is used and analysed using conventional PCA with Hotelling’s T2 and the squared prediction error (SPE). MSPCA with Hotelling’s T2 and SPE is used to improve the efficiency of fault detection and monitoring performance in conventional PCA. Therefore, MSPCA is successful in improving conventional PCA in fault detection and monitoring by reducing false alarms.