Sparse-Representation-Network-Based Feature Learning of Vibration Signal for Machinery Fault Diagnosis

Although deep neural networks (DNNs) have been widely applied in machinery fault diagnosis, the key problems of impulsive component extraction and noise filtering in the learning procedure are not addressed very well. Thus, a sparse representation network (SRNet) is developed to extract impulses from collected signals and then used for machinery fault recognition. For the purpose of improving the feature extraction capacity of SRNet, a convolutional sparse graph is developed in a sparse representation layer to suppress noise and reserve impulsive characteristics of signals. A selective residual learning is developed to effectively optimize gradient propagation and further enhance the feature learning performance of SRNet. Finally, the feature learning and fault classification capacity of SRNet is evaluated on two gearbox cases. The recognition accuracies of SRNet are 98.72% and 99.68% in two cases, respectively, demonstrating the effectiveness of SRNet compared with other DNNs.