Signal-to-Noise Ratio Based Fault Detection and Identification

In this work, we introduce signal-to-noise ratio (SNR) based fault detection and identification mechanisms for a networked control system feedback loop, where the network component is represented by an additive white noise (AWN) channel. The SNR approach is known to be a steady-state analysis and design tool, thus we first introduce a finite time approximation for the estimated AWN channel SNR. We then consider the case of a general linear time-invariant plant model with one unstable pole. The potential faults that we discuss here cover simultaneously the plant model gain and/or the unstable pole. The fault detection is performed relative to the estimated AWN channel SNR. The fault identification is performed using recursive least squares ideas and then further validated with the observed SNR value, when a fault has been previously detected. We show that the proposed SNR-based fault mechanism (fault detection plus fault identification) is capable of processing the proposed faults. We conclude discussing future research based on the contributions exposed in the present work.