A Fault Diagnosis Method for Planetary Gearbox Based on GAF-CNN

被引：0
|
作者：
Pang X.-Y. [1 ,2 ]
Tong Y. [1 ,2 ]
Wei Z.-H. [1 ,2 ]
机构：
[1] College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan
[2] Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, Taiyuan
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2020年 / 40卷 / 11期
关键词：
Convolutional neural network(CNN); Fault diagnosis; Gram angle field(GAF); Image encoding; Planetary gearbox;
D O I：
10.15918/j.tbit1001-0645.2020.064
中图分类号：
学科分类号：
摘要：
In order to apply the advantages of deep learning to recognize 2D images for the fault diagnosis of planetary gearboxes, a fault diagnosis model of planetary gearboxes based on gram angle field-convolution neural network (GAF-CNN) was proposed. Using two methods, gram angle difference field (GADF) and Gram angle sum field (GASF) in the gram angle field (GAF), the planetary gearbox vibration signal was converted into a 2D image, and the image features were extracted and input into the optimized CNN model, and the ideal recognition accuracy was finally obtained. Analyzing the influence of network parameters and different network layers on the fault diagnosis model, an optimal model combination was carried out. The test and comparison analysis results show that GADF-CNN can provide higher recognition accuracy than GASF-CNN; GADF-CNN is superior to other intelligent algorithms in the faults diagnosis of planetary gearbox. © 2020, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：1161 / 1167
页数：6
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