| 适用于轴承故障诊断的数据增强算法 |
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| 引用本文： | 林荣来,汤冰影,陈明. 适用于轴承故障诊断的数据增强算法[J]. 计算机工程与应用, 2021, 57(7): 269-278. DOI: 10.3778/j.issn.1002-8331.2006-0030 |
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| 作者姓名： | 林荣来 汤冰影 陈明 |
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| 作者单位： | 同济大学 机械与能源工程学院,上海 201804 |
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| 摘 要： | 针对在轴承故障诊断中存在的故障数据较少、数据所属工况较多的问题,提出了一种基于阶次跟踪的数据增强算法.该算法利用阶次跟踪中的角域不变性,对原始振动信号进行时域重采样从而生成模拟信号,随后重新计算信号的幅值来抵消时域重采样以及环境噪声对原始信号能量的影响,最后使用随机零填充来保证信号在变化过程中采样长度不变.对比实验表明...
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| 关 键 词： | 数据增强 信号处理 故障诊断 阶次跟踪 |
Data Augmentation Algorithm for Bearings Faults Diagnosis |
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| LIN Ronglai,TANG Bingying,CHEN Ming. Data Augmentation Algorithm for Bearings Faults Diagnosis[J]. Computer Engineering and Applications, 2021, 57(7): 269-278. DOI: 10.3778/j.issn.1002-8331.2006-0030 |
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| Authors: | LIN Ronglai TANG Bingying CHEN Ming |
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| Affiliation: | School of Mechanical Engineering, Tongji University, Shanghai 201804, China |
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| Abstract: | To address the problem of the inadequate faulty examples and various working conditions of industrial data in bearings faults diagnosis, a practical data augmentation based on order tracking is proposed. With the use of the angular invariance of order tracking, original signals are resampled in time domain in order to obtain the simulated signal that having the sharing pattern in angular domain. Then, the amplitude of the signal is recalculated to offset the energy change causing by resampling and environment noise. Finally, random zero padding is utilized to make the length of the signal consistently. According to experimental results, the newly proposed algorithm can increase the sample diversity and the quantity of dataset. Besides, it can alleviate existing problems in original dataset and effectively improve the classification accuracy and the generalization performance of diagnosis models. |
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| Keywords: | data augmentation signal processing fault diagnosis order tracking |
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