A sparse denoising deep neural network for improving fault diagnosis performance

Deep neural network (DNN) has been recently used in the field of fault diagnosis, but still their applicability is restricted to high computational complexity. In addition, useless information transformation between adjacent layers of the network could have a negative influence on the diagnosis accuracy. In this paper, a new DNN structure with sparse gate is designed to highlight the role of neurons contributed more by making it directly transfer through layers rather than transfer via an activation function. So it can reduce the computational complexity of network training since only those contributed less are required to be transferred via a nonlinear transformation. The proposed sparse denoising DNN (SD-DNN)-based fault diagnosis method can achieve more accurate diagnosis result with less computational complexity. It shows significant superiority to other-related methods in the case when only small size of training samples polluted by strong noise is available, which is very common for the engineering field of fault diagnosis. The experimental testing of fault diagnosis for rolling bearings verifies the effectiveness of the proposed method.