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.

深度神经网络（DNN）最近已用于故障诊断领域，但其适用性仍受限于高计算复杂度。此外，网络相邻层之间无用的信息转换可能会对诊断准确性产生负面影响。在本文中，设计了一种具有稀疏门的新 DNN 结构，通过使其直接通过层进行传输而不是通过激活函数进行传输来突出神经元的作用。因此它可以降低网络训练的计算复杂度，因为只有那些贡献较少的才需要通过非线性变换进行传输。所提出的基于稀疏去噪DNN（SD-DNN）的故障诊断方法可以以较少的计算复杂度获得更准确的诊断结果。在只有小规模受强噪声污染的训练样本可用的情况下，它比其他相关方法显示出显着的优越性，这在故障诊断工程领域非常普遍。滚动轴承故障诊断实验验证了该方法的有效性。