Improvement of Process State Recognition Performance by Noise Reduction with Smoothing Methods

Abstract

Multivariate statistical process control (MSPC) is important for monitoring multiple process variables and their relationships while controlling chemical and industrial plants efficiently and stably. Although many MSPC methods have been developed to improve the accuracy of fault detection, noise in the operating data, such as measurement noise and sensor noise, conceals important variations in process variables. This noise makes it difficult to recognize process states, but has not been fully considered in traditional MSPC methods. In this study, to improve the process state recognition performance, we apply several smoothing methods to each process variable. The best smoothing method and its hyperparameters are selected based on the normal distribution and variation of the reduced noise. Through case studies using numerical data and dynamic simulation data from a virtual plant, it is confirmed that the fault detection and identification accuracy are improved using the proposed method, which leads to enhanced state recognition performance.