Hierarchical diagnosis network based on sparse deep neural networks and its application in bearing fault diagnosis

As a breakthrough in artificial intelligence, deep learning allows for the automatic extraction of features without considerable prior knowledge and the determination of the complex non-linear relationship of the input parameters. Owing to these advantages, deep neural networks (DNNs) are superior to traditional artificial neural networks with shallow architectures, and are thus becoming widely used in the fault diagnosis field. To overcome the difficulties in bearing fault type classification and severity diagnosis, a two-layer hierarchical fault diagnosis network based on sparse DNNs is presented in this paper. In the proposed hierarchical fault diagnosis network, the first layer is responsible for fault type identification, and the second layer is responsible for fault severity diagnosis. An autoregressive (AR) model is established using empirical mode decomposition (EMD) to obtain the AR parameters as the input vectors of the proposed diagnosis network. The AR parameters are regarded as the low-level features which are helpful for the diagnosis network mine more useful high-level features. Experiments and comparison analyses are conducted to verify the performance of the proposed hierarchical fault diagnosis network. Results fully demonstrate the superiority of the proposed hierarchical fault diagnosis network in fault type classification or fault severity diagnosis.