Multiple Fault Isolation to a Servo-Valve Controlled Motor Transmission System

Abstract

Model based quantitative fault diagnosis procedure is based on generation of fault indicators, structural analysis and finally estimation of parameters. In this article, bond graph modelling is used to describe the system model and generate fault indicators by evaluating a set of Analytical Redundancy Relations (ARR), which are obtained from differentially causalled bond graph model to satisfy inverse system dynamics. Multiple fault case is realised by changing the parameters simultaneously. Thereafter the parameters related only to unstructured part of the fault subspace are estimated by optimising an objective function containing ARRs. Sometimes the derivative form of ARRs creates problem of singularity in estimation, and to avoid such a singularity problem we have estimated the corresponding parameters directly from constraint relations and thereafter those values are called in the main objective function to estimate rest of the parameters. The algorithm provides quicker fault isolation because it does not need several model simulations; thereby making it suitable for real-time process supervision.