Abstract
In this paper, we present an way for railway bearing fault diagnosis with the use of FIR-wavelet packet and LVQ neural network, First, the original vibration signal of trains' rolling bearing is denoised based on FIR. Then, the sig- nals after de-noised are preprocessed by wavelet packet and the wavelet packet energy eigenvector is reconstructed, those kinds of wavelet packet energy eigenvectors are used to train LVQ neural network. Finally, the intelligent fault diagnosis is realized. The result shows that this approach is effective to distinguish this kind of rolling bearing faults. This method has important practical value.
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