国外文献 > 1.轴承 > 5. 监测与诊断 > 故障诊断
关键词搜索：
| 标 题： |
A bearing fault image recognition method based on reliability analysis and RSO-optimized Alexnet
基于可靠性分析与RSO优化Alexnet的轴承故障图像识别方法 |
| 作 者： |
Shangbin Gao
Jiale Li
Zhun Zhang |
| 关键词： |
Bearings [轴承]
Fault diagnosis [ 故障诊断]
Alexnet model [ AlexNetNet 模型]
Swarm optimization algorithm [ 群智能优化算法]
Progressive grid method [ 渐进网格法] |
| 出版年： |
2023 |
| 文集名： |
2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE 2023)
2023年IEEE国际电气、自动化与计算机工程会议（ICEACE 2023） |
| 会议名： |
2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE 2023)
2023年IEEE国际电气、自动化与计算机工程会议（ICEACE 2023） |
| 届 次： |
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| 摘 要： |
Aiming at the problem of insufficient feature extraction ability and credibility loss in the recognition of bearing fault image, a bearing fault diagnosis method based on credibility analysis and RSO (Rat Swarm Optimizer) optimized Alexnet is proposed. Firstly, the collected bearing original vibration signal data is converted to a time-frequency graph by the method of signal conversion to image, and generate data sets. Secondly, the noise resistance of various lightweight network frames is tested, and the optimal network frame is obtained. Then, the parameters of the network framework are compared and adjusted by the progressive mesh method and the RSO algorithm. Finally, the interpretability and reliability characteristics of the network framework are illustrated by the Grad-CAM method. The experimental results of Case Western Reserve University bearing data set show that the proposed method can achieve close to 100% bearing fault image recognition accuracy and good anti-noise performance, and has excellent application prospects.
针对轴承故障图像识别中特征提取能力不足和可信度下降的问题，提出了一种基于可信度分析与RSO（蚁群优化器）优化AlexNet的轴承故障诊断方法。首先，将采集到的轴承原始振动信号数据通过信号转图像的方法转换为时频图，并生成数据集。其次，对多种轻量级网络结构的抗噪性能进行测试，获得最优网络结构。然后，采用逐步网格法与RSO算法对网络框架参数进行比较和调整。最后，利用Grad-CAM方法展示该网络结构的可解释性和可靠性特征。实验结果表明，所提方法在Case Western Reserve大学轴承数据集上的故障图像识别准确率接近100%，具有良好的抗噪性能，具备优异的应用前景。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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