Abstract
Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. At first, the convolution layer is used to extract the features of the original signal. Second, the generalization ability of the model is improved by replacing the full connection layer with the global average pooling layer. Then, the extracted features are classified by a light gradient boosting machine. Finally, the verification experiment is carried out, and the experimental result shows that the average training and diagnosis time of the model is only 39.73 s and 0.09 s, respectively, and the average classification accuracy of the model is 99.72% and 95.62%, respectively, on the same and variable load test sets, which indicates that the diagnostic efficiency and classification accuracy of the proposed model are better than those of other comparison models.
Highlights
Rolling bearing is one of the most critical components widely used in a modern machine, and it is easy to appear cracks, pitting corrosion, and other local damages or defects on the inner and outer ring raceways and rolling elements of the rolling bearings under the harsh working conditions of high temperature, alternating load, and long-time fatigue
In order to overcome the slow convergence speed, poor global search ability, and difficult designing rotation angle of quantum-inspired evolutionary algorithm (QEA), Xing et al [1] proposed an improved quantum-inspired cooperative coevolutionary algorithm, named MSQCCEA, which is based on combining the strategies of cooperative coevolution, random rotation direction, and Hamming adaptive rotation angle, and the results demonstrate that the proposed MSQCCEA has faster convergence speed and higher convergence accuracy
In order to overcome the low solution efficiency, insufficient diversity in the later search stage, slow convergence speed, and a high search stagnation possibility of differential evolution (DE) algorithm, Deng et al [2] studied the quantum computing characteristics of quantum evolutionary algorithm (QEA) and, combined with the divide and conquer idea of cooperative evolutionary algorithm (CCEA), proposed an improved differential evolutionary algorithm (HMCFQDE), and the results proved that the proposed HMCFQDE has higher convergence accuracy and stronger stability and a strong ability to optimize high-dimensional complex functions
Summary
Rolling bearing is one of the most critical components widely used in a modern machine, and it is easy to appear cracks, pitting corrosion, and other local damages or defects on the inner and outer ring raceways and rolling elements of the rolling bearings under the harsh working conditions of high temperature, alternating load, and long-time fatigue. In order to solve the above problems, in this study, a bearing fault diagnosis model combined with LightGBM algorithm and the improved convolutional neural network that is optimized by replacing the full connection layer to the global average pooling (GAP) layer is proposed (hereinafter referred to as GCNN). E pooling layer is mainly used to select and filter the feature graph extracted from the convolution layer and replace the results of a single point in the feature graph with the statistics of its adjacent regions so as to reduce the number of nodes in the final fully connected layer It can reduce the overfitting and improve the fault tolerance of the model. The bundled features are mutually exclusive so that the two features will not lose information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
