The contact damping between rough surfaces has an important influence on the wear, vibration, contact fatigue and energy dissipation between interfaces. In this paper, based on contact theory, a tangential damping mathematical model of rough surfaces is established from the point of view of viscous contact damping energy dissipation mechanism of asperities and considering the fractal characteristics of three-dimensional topography of rough surfaces. Through the combination of micro contact modeling and macro dynamic testing of composite beams, the analysis results show that there are important evolution rules between tangential damping and surface fractal parameters and material parameters. The nonlinear relations between them are as follows: tangential contact damping is positively correlated with normal load, load ratio and maximum contact area of asperity, and negatively correlated with fractal roughness; tangential contact damping increases first and then decreases with the increase of three-dimensional fractal dimension. The results of computational and experimental modal analysis show that the established mathematical model is feasible for predicting tangential damping. The study of tangential contact damping between surfaces can lay a foundation for improving the performance of assembly interfaces.
The research aiming at diagnosing rolling bearing fault is of great significance to the health management of equipment. In order to solve the problem that rolling bearings are faced with variable operating conditions and the fault features collected are single in actual operation, a new lightweight deep convolution neural network model FC-CLDCNN (FFT-CWT-A lightweight deep convolutional neural network composed of convolution pooling dropout group) with two-stream feature fusion and cross load adaptive characteristics is proposed for rolling bearing fault diagnosis. Firstly, the original vibration signal is transformed into one-dimensional frequency domain signal and two-dimensional time-frequency graph by FFT (Fast Fourier transform) and CWT (Continuous wavelet transform). Then, the one-dimensional frequency domain signal and two-dimensional time-frequency diagram are input into the two channels of the model respectively to extract and recognize the one-dimensional and two-dimensional features; Finally, one-dimensional and two-dimensional features are combined in the fusion layer, and fault types are classified in the Softmax layer. FC-CLDCNN has the characteristics of two-stream feature fusion, which can give full consideration to the characteristics of rolling bearing fault data, so as to achieve efficient and accurate identification. The Case Western Reserve University (CWRU) dataset is used for training and testing, and it is proved that the proposed model has high classification accuracy and excellent adaptability across loads. The Machinery Failure Prevention Technology (MFPT) dataset was used to validate the excellent diagnostic performance and generalization of the proposed model.
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