Research On Fault Diagnosis Of Key Components Of Spacecraft

With the development of aviation technology,the components of spacecraft are getting more and more sophisticated.However,the spacecraft shoulders more and more complex and high load missions.These changes exacerbate the possibility of spacecraft failure.The faults of arbitrary parts may lead to the failure of the space mission.In order to avoid disaster and reduce the possibility of space equipment fault,this paper proposes a fault diagnosis method for four components with high failure rate in spacecraft components,which is suitable for their respective characteristics and working environment.The main research contents include:(1)The second and third chapters introduce the method of fault diagnosis based on information.The second chapter is aimed at the fault diagnosis of the rolling bearing in the space equipment.Based on the angle of vibration signal,the time domain and frequency domain parameters are extracted simultaneously as characteristic parameters for analysis,and the rolling bearing condition is diagnosed by the hybrid neural network composed of BP neural network and perceptron network,so the fault mode can be judged directly according to the output result,and the simulation results show that the diagnostic efficiency of the hybrid network reached 94.5%,diagnosis time is reduced by 40%on the basis of the original.(2)In the third chapter,for the fault diagnosis of spacecraft power system,in order to realize the fault diagnosis of main power system of spacecraft,a method based on wavelet network is proposed.By comparing with the BP neural network,the structure of the wavelet network is simpler,the speed of convergence is faster,and the rate of miscarriage of justice is greatly reduced.(3)The fourth and fifth chapters introduce a model based fault diagnosis method.In the fourth chapter,the fault diagnosis of the spacecraft rotor system is studied.The spacecraft rotor can be transformed into a stochastic systems with unknown parameters,in this system,the rolling algorithm is designed based on the Kalman filter theory and LQG control method.By comparing the attenuation rate of the parameters with the threshold value,the fault of the spacecraft rotor system can be judged.The simulation results show that the method can detect and separate the faults accurately,and also has good fault tolerance performance.(4)The fifth chapter studies the fault diagnosis of the attitude sensor.Attitude sensor can be simplified to a stochastic system with multiple unknown parameters.In view of the complexity,mutation and other characteristics of its fault mode,the multi-model theory is introduced.In the fifth chapter,Based on the theory of multiple models,it is concluded that the dual control model can accurately detect the fault of attitude sensor,Even for superimposed faults,the diagnostic methods in this chapter are equally effective.Even for superimposed faults,the method of diagnosis in this chapter is also effective.Bayesian posteriori probability is used to analyze the operation effect of the system,and the rationality of multi-model fault diagnostics is verified.Finally,this paper summarizes the whole text and further points out the problems to be studied and perfected.