ARR Based System Mode Identification

ARR-based system mode identification has been illustrated for the boost converter with only two modes and for the three-phase diode bridge inverter with six active... 

In online model-based FDI, ARRs are evaluated using measurements from the real system being subject to disturbances. The time evolution of ARR residuals serve as fault indicators. For hybrid systems, ARRs are system mode dependent. Hence, an unobserved mode change invalidates the actual set of ARRs. As a result, computed values of fault indicators may exceed current thresholds indicating faults in some system components that have not happened. ARR residuals derived from a bond graph can not only serve as fault indicators but may also be used for model-based system mode identification. 

Chapter demonstrates that ARR residuals cannot only serve as fault indicators but may also be used for system mode identification in online FDI. First, the general case of switched LTI systems is considered and it is assumed that ARRs can be expressed in explicit form relating known system inputs and inputs either obtained by measurements or by simulation of the real system behaviour. A small example illustrates bond graph based system mode identification using ARRs. 

Bond Graph Based System Mode Identification Using ARRs... 

Furthermore, an all-mode structural FSM holds for all modes. That is, from this FSM one FSM for each mode can be obtained. In online FDI, the current system mode must be identified from measured system outputs in order to use the correct FSM. Chapter presents an ARR-based approach to system mode identification. 

ARRs derived from hybrid system models are mode-dependent. For ARR-based FDI it is therefore necessary to know the current system mode so that ARRs with the correct set of discrete switch state values are evaluated. When ARR residuals are outside admissible parameter uncertainty bounds it is not clear whether a parametric fault has occurred or a mode change has happened. If a mode change has happened then an evaluation of ARRs with the discrete switch states of the last known mode yields wrong and misleading results. This chapter considers system mode identification for healthy systems only. For the more complex case of system mode changes in the presence of faults, it is suggested to see latest publications, e.g. [4],... 

The focus of this book has been on the presentation of a bond graph model-based approach to FDI and failure prognosis for hybrid systems. It turns out that ARRs derived from a bond graph play a key role in all tasks that have been considered, in FDI, in system mode identification and in failure prognosis. Simulation results have been obtained by using the dassl solver as part of the open source software Scilab [5]. 

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