Approaches To Bearing Fault Diagnosis And On-board Diagnosis System For Subway Trains

The rapid development, large-scale construction and operation of subway in our country has greatly improved the current situation of our urban traffic, but it also brings challenges to the subway security at the same time. The proportion of mechanical failure is highest in subway vehicle fault statistics, and bearing failure occupies a higher proportion of mechanical failure. Bearing fault not only affects the driving safety of subway vehicle, it may also lead to casualties and economic losses, and causes serious social impact, so is necessary to research on-board diagnosis system of bearing fault.There are many bearing fault diagnosis systems of domestic trial-manufacture, but because of their own defects, very few of them are eventually formig products and installed on the rail transit vehicle to monitor the bearing operative state, so the diagnosis product installed on the subway train is much less. The paper emphasizes on the research of the bearing on-board fault diagnosis technologies and system of urban rail transit, and carries out the following task:1. A new bearing fault diagnosis method is proposed based on Adaptive Fourier decomposition. Adaptive Fourier decomposition is applied to decompose the original bearing vibration signal into a series of mono-components, then part of mono-components with fault imformation is extracted and summed as the bearing fault signal, which is processinged by demodulated resonance technology, and finally the the bearing fault is identified from the obtained frequency spectrum.2. A new bearing fault diagnosis model is proposed. The bearing model is constructed based on the bearing physical structure and force analysis. The contact of each bearing component is supposed as a sping-mass-damping system, and the affect of the number change of the pressure-bearing rollers is considered to contructed the bearing model. Then the bearing fault is described as an extra force and embedded into the bearing model. Finally the bearing fault diagnosis method based on adaptive Fourier decomposition is applied to verify the effectiveness of the proposed model.3. The paper proposes a new bearing fault diagnosis method based on the fusion of time-domain parameters and frequency-domain parameters. It analyzes the advantage and disadvantage of time-domain parameters and frequency-domain parameters applied to diagnose the bearing fault, and proposes a new bearing fault diagnosis method based on the fusion of time-domain parameters and frequency-domain parameters, which has both the advantage of the two kinds of parameters. The new bearing fault diagnosis method is verified by experiment data.4. The paper designs the bearing fault on-road diagnosis system of urban rail transit vehicle by combining the existing monitoring equipment on Guangzhou subway trains and the fault diagnosis algorithm based on the fusion of time-domain parameters and frequency-domain parameters. Finally the effectiveness and real-time of the system’s algorithm is verified by experiment.