The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm.

This paper investigates the contact stress induced by a rigid sphere sliding on a coating-ceramic system. A discrete element model incorporating a ceramic substrate, a surface coating, and a rigid sphere is developed. The influences of the coating grain elastic modulus, coating surface friction coefficient, coating thickness, and interface microcrack defects on the stress distribution within the system are analyzed. The results indicate that a higher coating-to-substrate elastic modulus ratio increases the overall stress but reduces the interfacial shear stress. A lower surface friction coefficient is more beneficial for hard coatings. The relatively optimal coating thickness (h/a) is approximately 0.5. When interface microcrack defects are present, stress concentrations occur at their locations. Longer interface microcracks lead to greater stress concentration, and the interfacial concentrated stress increases with crack length.