| 黄庆卿,胡欣堪,韩 延,林志超,张 焱.多源域子域自适应的滚动轴承剩余寿命预测方法[J].电子测量与仪器学报,2022,36(10):100-107 |
| 多源域子域自适应的滚动轴承剩余寿命预测方法 |
| Remain useful life prediction of rolling bearing based onmulti-source subdomain adaption network |
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| DOI： |
| 中文关键词: 滚动轴承 剩余使用寿命 多源域 子域自适应 |
| 英文关键词:rolling bearing RUL multi-source sub-domain adaption |
| 基金项目:国家自然科学基金（51605065）、重庆市博士后科学基金（cstc2021jcyj bshX0094）、重庆市教委科学技术研究（KJQN202100612,KJQN202000611）项目资助 |
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| Author | Institution |
| Huang Qingqing | 1. Automation College, Chongqing University of Posts and Telecommunications,2. Institute of Industrial Internet, Chongqing University of Posts and Telecommunications |
| Hu Xinkan | 1. Automation College, Chongqing University of Posts and Telecommunications |
| Han Yan | 1. Automation College, Chongqing University of Posts and Telecommunications,2. Institute of Industrial Internet, Chongqing University of Posts and Telecommunications |
| Lin Zhichao | 1. Automation College, Chongqing University of Posts and Telecommunications |
| Zhang Yan | 1. Automation College, Chongqing University of Posts and Telecommunications,2. Institute of Industrial Internet, Chongqing University of Posts and Telecommunications |
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| 中文摘要: |
| 针对单一源域信息有限、域自适应对齐粒度不足导致滚动轴承剩余寿命( remain useful life, RUL)预测精度低的问题,
提出了一种多源域子域自适应(multi-source subdomain adaption network, MS_SAN)的滚动轴承剩余寿命预测方法。 首先,将采
集的原始振动信号进行快速傅里叶变换得到频域信号作为模型的输入。 其次,利用一维卷积将多个源域与目标域数据映射到
一个公共的特征空间,采用局部最大均值差异将每个源域与目标域的退化阶段在独立的特征空间进行领域自适应,缩小多个源
域与目标域之间的分布差异。 最后,通过综合各领域 RUL 预测模块的输出得到最终轴承剩余寿命预测结果。 在 PHM2012 数
据集上的测试结果表明该方法的预测准确率高于对比方法,能够对滚动轴承剩余寿命进行有效的预测。 |
| 英文摘要: |
| To address the problem that low accuracy of rolling bearing remain useful life ( RUL) prediction caused by the limited
information of single source domain and the insufficient granularity of domain, a new method of RUL for rolling bearing based on multisource subdomain adaption network is proposed. Firstly, fast fourier transform is applied to the collected raw vibration signals to obtain
the frequency-domain signals and it takes the frequency-domain signals as the input of the model. Secondly, to reduce the distribution
difference between multiple source domains and target domains, all domains are mapped to a common feature space by one-dimensional
convolution, and the local maximum mean discrepancy is used to align the degradation stage of each source domain and target domain in
an independent feature space. Finally, the RUL of rolling bearing is obtained by comprehensive output of the module in different
domains. The results on PHM2012 data set show that the prediction accuracy of proposed method is higher than the comparison method,
and can effectively predict the RUL of rolling bearing. |
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