One Dimensional Convolutional Neural Networks Using Sparse Wavelet Decomposition for Bearing Fault Diagnosis

This paper proposes a novel algorithm for bearing fault diagnosis using sparse wavelet decomposition for feature extraction combined with a multi-scale one dimensional convolutional neural network (1-D CNN). The proposed algorithm consists of three stages. The first stage determines bearing fault frequency bands according to bearing physical parameters and constructs a sparse wavelet decomposition model. The second stage decomposes a raw bearing signal into multi-resolution signals based on a decomposition structure achieved at the second stage. Finally, the decomposed multi-resolution signal features are fed into the sub-neural networks according to the multi-scale 1-D CNN (MSCNN) network, and then the outputs of the final convolutional/polling layers are concatenated into a single channel which is further used as the input to a fully connected layer. In comparison with the other bearing fault diagnosis methods, our proposed algorithm can achieve a higher classification accuracy of 99.85% using the Case Western Reserve University (CWRU) bearing dataset. The proposed algorithm is successfully validated via our designed experiments.