A non-negative garrote shrinkage network with adaptive Swish for rotating machinery fault diagnosis under noisy environment

The strong noise existing in the vibration signals has a negative impact on rotating machinery fault diagnosis. To solve the noise problem in the engineering applications of fault diagnosis, a deep residual network, named non-negative garrote shrinkage network with adaptive Swish (NNGSN-AS), is proposed for rotating machinery fault diagnosis under noisy environment. In the NNGSN-AS, the non-negative garrote shrinkage function (NNGSF) is integrated into residual building blocks as nonlinear transformation layers, and the residual building block is named the non-negative garrote shrinkage building unit (NNGSBU). In the NNGSBU, the threshold of the NNGSF is adaptively learned by the thresholding module, so that different thresholds can be assigned to different data samples. The thresholding module is close to the NNGSBU input, enabling early noise handling. The depthwise convolutions with wide kernels in the thresholding module increase the receptive field and lead to a one-to-one correspondence between the learnable threshold and the input feature elements of the NNGSBU, reducing the negative influence of the noise. Additionally, an adaptive Swish (ASwish) activation function module is developed, enabling adaptive nonlinear transformation of each feature channel. The experimental results on a public dataset and our laboratory dataset indicate that the NNGSN-AS is superior to the existing methods for rotating machinery fault diagnosis under noisy environment. Given that the performance gain of the NNGSN-AS stems from multiple components for deep feature extraction, the ablation experiments are conducted to demonstrate the improvement effect of each component.