Aiming at the problem that the time-frequency image of bearing fault characteristics is relatively weak and difficult to identify. This paper presents a time-frequency analysis method of local maximum synchrosqueezing transform based on image enhancement. Firstly, the instantaneous frequency of the collected vibration signal is obtained through local maximum synchrosqueezing transformation. Secondly, a local histogram cropping equalization image enhancement algorithm is proposed, which is used to obtain time-frequency images with clearer textures. Then, in order to extract fault features from the enhanced instantaneous frequency (IF) image, A new neural network is proposed. The network consists of Multi-size convolution kernel module, Dual-channel pooling layer and Cross Stage Partial Network (MDCNet). Finally, the fault signal was collected on the bearing fault test bench for prediction, and the accuracy rate reached 99.7%. And compared with AlexNet, VGG-16, Resnet and other methods. The results show that the method can meet the needs of actual engineering.INDEX TERMS Fault diagnosis, image enhancement, instantaneous frequency.

In recent years, residual network has been widely used in the field of intelligent diagnosis because of its powerful function. This paper proposes a novel dense residual network (DRNet), which combines the advantages of dense connections and residual learning to prevent gradient disappearance and network degradation caused by network deepening for efficient fault diagnosis of rolling bearings. First, each sub-block in the dense network is deeply processed so that it has better nonlinear expressive ability to extract deep fault features. Then, the residual learning is embedded in each sub-block of the dense network, so that each sub-block processed by deepening will not show the phenomenon of network degradation. Finally, an Adam-Subtracted momentum (Adam-S) optimization algorithm is proposed, which adds the first-order momentum and the second-order momentum of the previous gradient into the expression of the second-order momentum of the current gradient, which enhances the connection between the parameters in the two adjacent gradients in the Adam algorithm. It makes the algorithm more reliable and the gradient prediction more accurate. Without adding additional parameters, the training stability of the algorithm in complex environments is further improved. Experiments on two kinds of data sets under different working conditions are carried out for many times, and comparison with Random Forest, Support Vector Machine, Dense Network, Residual Network, AlexNet and DRNet-Adam proves the effectiveness and feasibility of the proposed model and optimization algorithm.

Bearing is a key part of rotating machinery. Accurate prediction of bearing life can avoid serious failures. To address the current problem of low accuracy and poor predictability of bearing life prediction, a bearing life prediction method based on digital twins is proposed. Firstly, the vibration signals of rolling bearings are collected, and the time-domain and frequency-domain features of the actual data set are extracted to construct the feature matrix. Then unsupervised classification and feature selection are carried out by improving the self-organizing feature mapping method. Using sensitive features to construct a twin dataset framework and using the integrated learning CatBoost method to supplement the missing data sets, a complete digital twin dataset is formed. Secondly, important information is extracted through macro and micro attention mechanisms to achieve weight amplification. The life prediction of rolling bearing is realized by using fusion features. Finally, the proposed method is verified by experiments. The experimental results show that this method can predict the bearing life with a limited amount of measured data, which is superior to other prediction methods and can provide a new idea for the health prediction and management of mechanical components.