Transactions of Nanjing University of Aeronautics & Astronautics
Rotor Fault Diagnosis Based on Weighted D-S Evidence Theory
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Abstract:
The main rotor is the lift surface and control surface of a helicopter， and its normal health is crucial for the safety of the helicopter. The rotor fault diagnosis technology is still a weak link in the field of helicopter health and usage monitoring system （HUMS）， and the development of rotor fault diagnosis technology is of great value. Based on information fusion technology， the mechanism of rotor failure is analyzed， the rotor failure model is established， and the fault feature information of blades， hub and airframe under different faults are obtained by fluid structure coupled simulation， thus generating data sets for network training and verification. Then genetic algorithm-backpropagation （GA-BP） neural network is used to diagnose three types of helicopter rotor faults， namely， misadjusted trim-tab， misadjusted pitch control rod and imbalanced mass. Three cascaded levels of networks are used to identify fault classification， location and severity， respectively. Finally， the rotor faults are diagnosed and analyzed by the weighted Dempster-Shafer （D-S） evidence theory. The results demonstrate that the rotor blade fault diagnosis method based on the improved D-S evidence theory can be successfully applied to rotor blade fault diagnosis with good identification results.
Keywords:
Project Supported:
The work was supported by the National Key Laboratory of Helicopter Aeromechanics （No.61422202104）.
Clc Number:
V211.52
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