PROPELLER FAULT DIAGNOSIS BASED ON A RANK PARTICLE FILTER FOR AUTONOMOUS UNDERWATER VEHICLES

Rank particle filtering was applied to fault diagnosis technology. Control force and yaw moment losses that occurred in the corresponding degrees of freedom were estimated, and the trends of change were calculated by the multi-step-ahead prediction process in the rank particle filter. Based on the estimated results in the normal state, the situations involving failure were detected. To achieve this target, a mathematical model of the normal and faulty motion states of an underwater vehicle was first developed. Subsequently, the rank sample method combined with the particle filter was used, to obtain the importance probability density function of the autonomous underwater vehicle status. The rank particle filter obtained above realized the real-time state estimation and trend prediction of the motion state. A modified Bayesian algorithm was used to process the estimated control force and yaw moment losses in a given length of time. Based on the calculation results in the normal situations, a back propagation neural network was trained to obtain the diagnostic values. The condition of the propellers was determined based on the diagnostic values. Fault diagnosis simulation experiments were carried out using both the data obtained from the semi-physical simulation system with a hypothetical propeller failure, and the real sea trail data to verify the performance of the proposed algorithm. The results showed that the rank particle filter method could be applied to the propeller fault diagnosis of autonomous underwater vehicles, and solved the problem that arises when a single degree of freedom of the loss is utilized