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基于仿真数据的无监督域适应轴承故障诊断方法

Unsupervised domain-adaptive bearing fault diagnosis method based on simulation data

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【作者】 陈钧钖袁逸萍陈彩凤

【Author】 CHEN Junyang;YUAN Yiping;CHEN Caifeng;School of Mechanical Engineering, Xinjiang University;

【通讯作者】 袁逸萍;

【机构】 新疆大学机械工程学院

【摘要】 针对目前实际诊断任务中难以获得有效的滚动轴承故障数据以及目前诊断模型泛化能力差的问题,文章提出一种基于动力学仿真与无监督领域自适应的故障诊断方法。首先建立滚动轴承动力学仿真模型,获得大量的仿真数据充当源域;然后使用无监督领域自适应的迁移学习故障诊断方法,在全局领域适配的基础上,引入最大最小化分类器差异的对抗学习策略,进一步减小了源域和目标域特征的条件分布差异;最后通过与其他迁移学习方法对比验证所提方法的可行性与优异性。

【Abstract】 Aiming at the current problem that it is difficult to obtain effective rolling bearing fault data in practical diagnostic tasks and the poor generalization ability of the current diagnostic model,a fault diagnosis method based on dynamics simulation and unsupervised domain adaptation is proposed.Firstly,a rolling bearing dynamics simulation model is established,and a large amount of simulation data is obtained to serve as the source domain.Then,an unsupervised domain-adaptive transfer learning fault diagnosis approach is used,which introduces an adversarial learning strategy that maximizes and minimizes classifier differences on basis of global domain adaptation to further reduce the conditional distribution differences between source and target domain features.Finally,the feasibility and excellence of the proposed method is verified by comparing it with other transfer learning methods.

【基金】 国家自然科学基金“不确定环境下制造车间多场景运行态势评估与调度机制研究”(71961029);新疆自治区科技计划项目-重点研发专项“面向纺纱工厂的数字孪生全过程生产管控关键技术研发”(2022B01057-2)
  • 【文献出处】 制造技术与机床 ,Manufacturing Technology & Machine Tool , 编辑部邮箱 ,2024年02期
  • 【分类号】TH133.3
  • 【下载频次】127
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