Abstract
As for the non-linearity and non-stationary characteristics of the vibration signals of urban railway gearbox, an efficient method for gearbox fault detection and diagnosis based on EMD (empirical mode decomposition) and Elman neural network is proposed. First of all, the original signals are decomposed into a number of IMFs (intrinsic mode func- tion) by EMD. Secondly, the feature vectors are constructed. Finally, these eigenvectors as fault samples input to the Elman neural network. The recognition results show that the EMD and Elman neural network is effective in railway gear- box fault diagnosis. This approach can be used as a useful tool for the rotating machinery fault diagnosis.
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