Abstract
Rolling bearings are the most fault-prone parts in rotating machinery. In order to find faults in time and reduce losses, this paper presents an intelligent diagnosis method for rolling bearings. At present, the deep residual network (RESNET) is the most widely used convolutional neural network (CNN) and has become one of the hotspots in fault diagnosis. However, the fully connected layer of the deep residual network has the disadvantage of too many training parameters, which makes the model training and testing time longer. So, we proposed a new network structure which the global average pooling (GAP) technology replaces the fully connected layer part of the traditional RESNET. It effectively solves the problem of too many parameters of the traditional RESNET model, and uses data enhancement, dropout, and other deep learning training techniques to prevent the model from overfitting. Experiments show that the accuracy of fault diagnosis of the improved algorithm reaches 99.83%, training time has been shortened. Also, the whole process of rolling bearing fault detection does not need any manually extract features, and this “end-to-end” algorithm has good versatility and operability.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The number of model parameters is greatly reduced and the training time is significantly reduced in the improved residual network (RESNET) algorithm because the full connection layer is removed, which is of great significance for the model to be applied to the online rapid diagnosis and monitoring of faults
In order to further show the ability of the improved Residual Network (ResNet) algorithm to identify minor faults and the details of the fault misjudgment, we introduce the multi-classification confusion matrices [37] to conduct a detailed quantitative analysis of the fault results
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Hydropower Units, Large wind power equipment, and other rotating machinery are developing towards high precision machinery field. A reliable health detecting system is key for the steady operation of mechanical equipment [1]. Rolling bearing affects the overall performance of Rotating machinery [2]. Fault diagnosis of rolling bearings has attracted more and more attention. It can minimize maintenance costs and increase system reliability [3]
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