Study Of Strategy Of The Fault Diagnosis On Main Reducer Of Micro-vehicle Drive Axle

This essay focus on the improvement of NVH quality of Micro-vehicle. In view of the rear axle main reducer fault vibration are the main sources of vehicle vibration and noise, this subject focus on main reducer. By using "The main reducer vibration testing system" detection the vibration signal of the main reducer after its machining and assembly are finished. By on-line monitoring the time domain signal parameters: waveform characteristic value, the pulse characteristic value, kurtosis value to indicate the quality of the main reducer. Removing the failure main reducer to prevent the failed main reducer move into the micro-vehicle assembly link.Analysis the fault vibration signal of the failure of main reducer by using off-line analysis to determine the fault type and cause of the failure of the main reducer. Considering the feature of non-stationary signals of the main reducer fault vibration signals, the essay using frequency domain analysis, wavelet analysis, Hilbert-Huang transform to analysis the vibration signal. By compare the pros and cons of different analysis methods theoretically and their application effect, proposed the most suitable signal processing method for this subject-Hilbert-Huang Transform and demonstrated the practicality of the Hilbert-Huang transform on dealing with the non-linear, non-stationary signal of main reducer from the theory of algorithms and experimental applications. Finally, by using the characteristic that intrinsic mode functions in Hilbert-Huang Transform can objectively reflect the fluctuations of the signal within the vibration signal due to the instantaneous impact, use the vibration signal energy value of intrinsic mode functions to build the fault vibration signal characteristic vector to indicate the dynamic characteristics of the main reducer.The subject established a fault diagnosis expert system based on the neural networks, use the fault signal eigenvector as input, the expert system classify the fault types of main reducer and use fault types of main reducer as output of the expert system, nonlinear mapping of the main reducer fault vibration signal eigenvectors to the fault type of the main reducer. Achieving the purpose of diagnose the failure of the main reducer by using its vibration signals, realize the automation and intelligence diagnosis the fault type of the main reducer.