Safe and reliable operation of mechanical equipment depends on timely and accurate fault diagnosis. When the actual working conditions are complex and variable and the available sample data set is small, recognition accuracy of the rolling bearing fault diagnosis model is low. To solve this problem, a novel method based on Markov transition field (MTF) and multi-dimension convolutional neural network (MDCNN) is proposed in this paper. Firstly, the original vibration signals are converted into two-dimensional images containing temporal correlation by MTF. Then, a neural network model is constructed by using multi-dimension attention (MDA) and E-Relu activation function to fully extract fault feature information. Finally, the MTF images are input into the model and the data is normalized using the group normalization method. The MDCNN model is validated on two different data sets, and the results show that compared with other intelligent fault diagnosis methods, the MDCNN has higher fault diagnosis accuracy and stronger robustness under both variable working conditions and small sample data sets conditions.

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The bearing dynamic behaviors will be complicated due to the changes in the geometric sizes and relative positions of the bearing components at high speed. In this paper, based on the Hertz contact theory, elastohydrodynamic lubrication (EHL) model, and Jones’ bearing theory, the comprehensive stiffness model of the angular contact ball bearing is proposed in consideration of the effects of elastic deformation, centrifugal deformation, thermal deformation, and the ball spinning motion. The influences of these factors on bearing dynamic stiffness are investigated in detail. The calculation results show that the centrifugal deformation and thermal deformation increase with the increase in rotation speed. When the centrifugal deformation and thermal deformation are considered, the bearing radial contact stiffness increases as the speed increases, whereas the axial contact stiffness and the angular contact stiffness decrease. When the deformations and the EHL are all considered, the comprehensive bearing stiffness decreases with the increasing speed. It is also found that the spinning motion of the ball causes the comprehensive bearing stiffness to increase.

Aiming at the problems of traditional fault diagnosis methods that do not represent the time correlation between signals, low recognition accuracy under complex working conditions and noise interference and too many parameters, a bearing fault diagnosis method based on mixed attention mechanism (MAM) and deep separable dilated convolution neural network (DSDCNN) is proposed. Firstly, a Markov transfer field (MTF) encoding method is used to transform the original one-dimensional vibration signal into a two-dimensional feature image with temporal correlation. Secondly, a deep separable convolution algorithm is presented by taking advantage of the low computational complexity of deep separable convolution and the ability of dilated convolution to expand the receptive field under the condition of invariable number of parameters. Then, the MAM is designed to make the model capture the feature dependency of the feature map in spatial and channel dimensions, and the MAM-DSDCNN model is constructed. Finally, the fault diagnosis performance of the proposed model is verified with two different data sets. The results show that the average recognition accuracy of MAM-DSDCNN reaches 99.63% under variable load conditions, 99.42% under variable speed conditions, 94.26% under noisy environment with the signal-to-noise (SNR) of 0dB, which prove that the model has higher recognition accuracy, stronger generalization and noise immunity performance than other deep learning algorithms.

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