A Cumulative Canonical Correlation Analysis-Based Sensor Precision Degradation Detection Method

Abstract

In practice, sensor precision degradation is ubiquitous and early detection of such a degradation is important for monitoring task. In this paper, a cumulative canonical correlation analysis (CCA) based sensor precision degradation detection method is presented in the Gaussian and non-Gaussian cases. At first, the fault sensitivity of the original CCA method to sensor precision degradation is theoretically analyzed. Then, the cumulative CCA-based method is proposed and delivers better fault detectability than the corresponding standard CCA-based method with respect to fault detection rate. For the non-Gaussian case, an efficient and practical applicable approach, referred as threshold learning approach, is proposed to set appropriate threshold based on available historical measurements. Finally, with the application to a real laboratorial continuous stirred tank heater plant, the feasibility and superiority of the proposed method are demonstrated by a comparison with the standard CCA-based and principal component analysis-based methods.