A New Approach to Multivariate Statistical Process Control and Its Application to Wastewater Treatment Process Monitoring - Ryo Namba; Takumi Obara; Yukio Hiraoka (International Journal of Prognostics and Health Management)
This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate Statistical Process Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. Firstly, a conventional MSPC, based on Principal Component Analysis (PCA), is adjusted to provide an easy-to-understand user interface and then a new yet simplified reconfigurable diagnosis model is introduced. The user interface that has been developed is designed to integrate MSPC seamlessly with existing process monitoring systems that use the so-called trend graphs. The proposed diagnosis model is constructed by aggregating small models with either one or two inputs, which enhances the tractability of the diagnosis model. The effectiveness of the modified MSPC is demonstrated through a series of offline and online experiments, using a set of real multivariate process data from a municipal wastewater treatment plant.