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This paper deals with fault detection and diagnosis scheme for stochastic non-linear systems using particle filter. To address the problem of fault detection of helicopters in the presence of sensor, actuator, and component faults, the algorithm uses a bank of particle filters running in parallel. The filter monitors the system states and identifies the occurrence of faults. Using the monitored system states, a log-likelihood ratio-based hypothesis testing is performed to detect and isolate faults in the system. Comparing log-likelihood ratio with threshold generated from deviation function of normal model induces the fault decision signal. The algorithm is applied to a 2 Degrees of Freedom helicopter system which is a highly complex, non-linear, and unstable system. The results are presented for sensor, actuator, and component faults represented as additive and multiplicative models. The results show the effectiveness of the algorithm compared with residual generation methods used in fault diagnosis.

Active fault diagnosis of 2 DoF helicopter using particle filter-based log-likelihood ratio