System identification and multi-fault isolation for a hydraulic drive with pump loading

Abstract

In this article, a least square optimization method for multi-fault detection and isolation has been revisited and validated through simulation and experimentation on a pedagogical hydrostatic transmission system. A nonlinear regression analysis has been made on the state equations, obtained from bond graph model of the system, to estimate the unknown parameters as a part of system identification. The model, assigned with the estimated and some known parameters, was then validated with the responses from the test rig. The rig was designed to impose faults (one at a time and/or simultaneously) in different components for the purpose of experimental validation of multi-fault detection and isolation. The model-based fault isolation was done using structural analysis of some constraint relations called analytical redundancy relations, the numerical evaluation of which is residuals. The robustness in fault isolation was addressed through linear fractional transformation approach to ensure residuals bounded within adaptive threshold under no fault situation. Finally, the isolated faulty parameters were estimated through particle swarm optimization algorithm for fault sizing. This article is directed towards corroboration of the existing fault isolation methodologies through experimentation on a power hydraulic circuit.