A fault diagnosis system of railway vehicles axle based on translation invariant wavelet

The railway vehicles are very important tools for transportation in the word, and the axles are key parts for its safety. Acoustic emission (AE) technique is more effective than vibration for detecting initiation fatigue in materials. The wavelet transform is a good signal analysis and treatment method for AE signals. But the wavelet threshold method may produce Pesudo-Gibbs phenomenon on the singularity points of signal. The translation invariant wavelet is an improve method based on that algorithm. Comparing with threshold method, a translation invariance method can suppress Pesudo-Gibbs phenomenon and minish RMSE between the original signal and estimated one. At the same time, SNR of estimated signal can also be improved. The acoustic emission energy method is adopted to identify the fatigue cracks of axle or railway vehicle. The results demonstrate that this method can effectively eliminate noise, extract characteristic information of acoustic emission signals, is effective for online detection of the fault.