Model-based diagnosis and prognosis of hybrid dynamical systems with dynamically updated parameters

Abstract

This chapter presents an integrated approach to model-based diagnosis and prognosis for hybrid dynamical system with sequential multiple fault of unknown nature or type. Bond graph modelling is used as a common framework for system modelling, virtual prototyping, fault diagnosis rule development, parameter and system identification, and remaining useful life (RUL) estimation. In a hybrid dynamical system, faulty discrete events may occur in addition to parametric faults of unknown natures/types. The procedure developed in this chapter can detect and isolate sequential multiple faults of different types, i.e., discrete mode faults, abrupt and progressive parametric faults; and also predict the RUL if the isolated fault is of progressive type. Residual sensitivity signature with the global fault sensitivity signature matrix (GFSSM) and mode change sensitivity signature matrix (MCSSM) is used to determine a smaller set of possible fault candidates after detection of a fault. It is shown that use of fault direction information from GFSSM and MCSSM improves the fault isolation process for discrete or parametric fault and also improves the parameter estimation process and RUL estimation. The proposed method is tested on a benchmark two-tank hybrid system model through simulation and is further validated with real experimental data collected from a reduced-scale equivalent hybrid electrical/electronic circuit model of the considered two-tank system.