Improving Fault Isolation and Identification for Hybrid Systems with Hybrid Possible Conflicts

Model-based fault isolation and identification in hybrid systems is computationally expensive or even unfeasible for complex systems due to the presence of uncertainty concerning the actual state, and also due to the presence of both discrete and parametric faults coupled with changing modes in the system. In this work we improve fault isolation and identification performance for hybrid systems diagnosis using Hybrid Possible Conflicts. The Hybrid Bond Graph modeling approach makes feasible to track system behavior without enumerating the complete set of system modes. Hybrid Possible Conflicts focus the analysis on potential mode changes on those subsystems whose behavior deviates from expected. Moreover, using information derived from the Hybrid Bond Graph model, we can cope with both discrete and parametric faults in a unique framework. Fault detection with Hybrid Possible Conflicts relied upon an statistical test to decide when a significant deviation in the residual occurs. Fault detection time was later used to start the fault isolation and identification stages. In this work we propose to analyze the evolution of the residual signal using CUSUM to find a more accurate estimation of the time of fault occurrence, which allows to improve both the potential new modes tracking and the parametric fault identification. Moreover, we extend our previous proposal for fault identification in continuous systems to cope with fault identification along a set of mode changes while performing parameter identification. We have tested these ideas in a four-tank hybrid system with satisfactory results.