Online sensor performance monitoring and fault detection for discrete linear parameter-varying systems

Abstract

This paper proposes a fault detection algorithm to identify online sensor performance degradation and failure, where the sensor faults are characterised by variations of the sensor measurement noise covariance matrix. That is, the proposed algorithm estimates the slowly-varying sensor measurement noise covariance and detects the abrupt and/or intermittent change of sensor measurement noise covariance. A memory-based technique is used to detect the abrupt (or intermittent) change of sensor noise covariance matrix. The memory-based technique is adopted due to its simplicity and online applicability. The proposed algorithm originally is designed for discrete linear time-varying (DLTV) systems and applied to discrete linear parameter-varying (DLPV) systems. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying and detecting the abrupt (or intermittent) change of sensor measurement noise covariance for multiple-input and multiple-output discrete linear parameter-varying systems, where the scheduling parameters lie within a compact set. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence.