LAUSR

In this paper, online fault detection and isolation (FDI) of multiple faults for a hybrid dynamical system based on partly decentralized model-based diagnosis architecture is presented. A quantitative model-based diagnosis method for both discrete and parametric faults occurring sequentially is proposed here, in which system global model is partitioned into various subsystems by analyzing the global fault sensitivity signature matrix (GFSSM) and mode change sensitivity signature matrix (MCSSM) of the global system. A local diagnoser is designed for each subsystem resulting in local GFSSM and MCSSM; and supervisory diagnoser is required only when if more than one local diagnoser generate alarm. The proposed method can also be used to solve the problem of sequentially occurring faults detection and isolation for a centralized or distributed architecture-based diagnosis. Residual sensitivity signature with the GFSSM and MCSSM is used to determine a smaller set of possible fault candidates after detection of a fault by the respective local diagnoser. 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 local parameter estimation process. The identified discrete fault or estimated parameters are then used to update the corresponding nominal model which allows for FDI of the next fault. For robust FDI, adaptive residual threshold is used which accounts for the process, measurement uncertainties and mode change, including the parameter estimation uncertainties. Algorithms and codes have been developed in industry preferred MATLAB-LabVIEW interface for real time monitoring of the hybrid system. The developed method is first tested on the four-tank hybrid system model through simulation. Then it is experimentally validated with real experimental data collected from a reduced-scale equivalent hybrid electronic circuit model of a bench mark two-tank system for checking the robustness of the proposed approach. The developed method is shown to produce accurate diagnosis of sequentially occurring faults in hybrid systems and saves valuable CPU time as compared with centralized approach.