Construction method of bearing health indicator based on multi-scale AlexNet network

被引：0
|
作者：
Zhang G. [1 ]
Tian F. [1 ]
Liang W. [1 ]
She B. [1 ]
机构：
[1] College of Weaponry Engineering, Naval University of Engineering, Wuhan
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 01期
关键词：
Bearing health indicator; Convolutional neural network; Deep learning; Evaluation metrics;
D O I：
10.3969/j.issn.1001-506X.2020.01.33
中图分类号：
学科分类号：
摘要：
The monotonicity and trendability of the health indicator constructed by the traditional method is not very ideal. To solve these problems, a bearing health indicator construction method based on multi-scale AlexNet network is proposed. First of all, the original vibrational acceleration signal is transformed to time-frequency map, which is considered as the input of the multi-scale AlexNet network, by continuous wavelet transformation. Then, the health indicator of test bearing is constructed online by the trained multi-scale AlexNet network. Finally, the constructed health indicator is evaluated by metrics, the evaluation results are then used to adjust the network parameters to realize the iterative optimization. The experiment results show that the health indicator constructed by the proposed health indicator construction method has better trendability and monotonicity. In addition, this method does not rely on feature extraction, selection and fusion, which enhances the construction efficiency and generalization. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：245 / 252
页数：7
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