Sensor fault detection and diagnosis in the presence of outliers

Abstract

In this paper, a sensor fault detection and diagnosis (FDD) method is proposed for linear state-space models in the presence of outliers. The t-distribution with unknown scale matrix and degrees of freedom (dof) parameter is used to describe the measurement noise. By using the variational Bayesian inference, the states, the scale matrix, and the dof parameter are estimated simultaneously. Since the noise distribution is no longer the Gaussian, a modified residual evaluation is proposed to detect the fault. After that, the cause of fault can be determined by observing the changes on measurement noise covariance. Two continuous stirred tank reactor (CSTR) process is conducted to demonstrate that the proposed method can provide more reliable FDD results than the existing methods when measurements contain outliers.