Diagnosis of parametric faults with optimal partitioning of frequency response estimates

Abstract

In this paper, a new frequency domain fault isolation method for linear time-invariant systems is proposed. A fault is assumed to be manifested in the change in one of the system parameters, which will in turn cause a change in the frequency response. Based on this changed frequency response, a fault detection is first made, then followed by a fault isolation which attempts to determine what fault has occurred. In practice, estimation of the frequency response is usually plaqued with noise, contributed from the estimation process as well as system and output noise. The statistical approach to the detection problem has been discussed in our earlier paper [10]. Here, we explore the issue of fault isolation after the detection phase. The proposed method captures the changes in the frequency response by monitoring an observation vector constructed from at least two segments of the frequency response. The changes in frequency response due to corresponding changes in the system parameters are first mapped out as trajectories in the vector plane. When a fault is detected, it is then isolated by comparing it to the reference trajectories. Throughout the fault isolation phase, only the frequency response of the system is estimated and no attempt is made to estimate the system parameters.