关键词: 滚动轴承, 故障诊断, 多尺度卷积, 注意力机制, 自适应网络, 局部最大均值差异

Abstract: To address the substantial differences in feature distribution under varying operating conditions and the limited domain adaptation capabilities in the fault diagnosis of rolling bearings, this paper proposes a fault diagnosis method that integrates a Hybrid Convolutional Neural Network (Hybrid Convolutional Neural Network, HCNN) with a Deep Subdomain Adaptation Network (Deep Subdomain Adaptation Network, DSAN) to effectively adapt to diverse operating conditions. Initially, vibration signals from bearings under different load conditions are collected and categorized into source and target domains. Subsequently, a hybrid convolutional neural network architecture, incorporating multi-scale convolution and attention mechanisms, is developed to extract fault characteristics. Feature distribution alignment between the source and target domains is achieved through a Gaussian Mixture Model (Gaussian Mixture Model, GMM) combined with a Local Maximum Mean Discrepancy (Local Maximum Mean Discrepancy, LMMD) loss function. Furthermore, an innovative weighting factor is introduced to enhance the convergence efficiency of the DSAN and optimize the diagnostic process. The proposed methodology is validated using the rolling bearing dataset from Huazhong University of Science and Technology. The experimental findings indicate that this methodology significantly improves the accuracy of fault diagnosis, demonstrating impressive performance.

Key words: rolling bearing, fault diagnosis, multi-scale convolution, attention mechanism, adaptive network, local maximum mean discrepancy