Fault Diagnosis Algorithm of Gearboxes Based on GWO-SCE Adaptive Multi-Threshold Segmentation and Subdomain Adaptation

The data distribution of the vibration signal under different speed conditions of the gearbox is different, which leads to reduced accuracy of fault diagnosis. In this regard, this paper proposes a deep transfer fault diagnosis algorithm combining adaptive multi-threshold segmentation and subdomain adaptation. First of all, in the data acquisition stage, a non-contact, easy-to-arrange, and low-cost sound pressure sensor is used to collect equipment signals, which effectively solves the problems of contact installation limitations and increasingly strict layout requirements faced by traditional vibration signal-based methods. The continuous wavelet transform (CWT) is then used to convert the original vibration signal of the device into time–frequency image samples. Further, to highlight the target fault characteristics of the samples, the gray wolf optimization algorithm (GWO) is combined with symmetric cross entropy (SCE) to perform adaptive multi-threshold segmentation on the image samples. A convolutional neural network (CNN) is then used to extract the common features of the source domain samples and the target domain samples. Additionally, the local maximum mean discrepancy (LMMD) is introduced into the parameter space of the deep fully connected layer of the network to align the sub-field edge distribution of deep features so as to reduce the distribution difference of sub-class fault features under different working conditions and improve the diagnostic accuracy of the model. Finally, to verify the effectiveness of the proposed diagnosis method, a fault preset experiment of the gearbox under variable speed conditions is carried out. The results show that compared to other diagnostic methods, the method in this paper has higher diagnostic accuracy and superiority.