This research develops a model-based particle filter algorithm for quickly detecting sudden faults in dynamic systems. Faults are defined as the abnormal behavior or failure of the system components. This novel method avoids the numerical issues of some other model-based methods. It also allows the fault magnitudes to take on continuous values, instead of constraining them to discrete values.
The multiple-model particle filter (MMPF) and interacting multiple-model particle filter (IMMPF) techniques are tested on a nuclear reactor pressurizer system for the detection of loss-of-coolant accidents (LOCA). The drawbacks of these methods leads us to the develop the novel algorithm: the state-augmented mutating particle filter (SAMPF), which uses random walk techniques. The SAMPF detects sudden faults faster than conventional random walk techniques. Choosing the proper parameters for the algorithm is considered. The performance of the SAMPF is compared to that of the IMMPF for the pressurizer system. The SAMPF is superior to the IMMPF in fault diagnosis accuracy and consistency
