| 马骥腾,吕卫民,李根,戴梓琴.基于不变风险最小化的未知工况轴承故障诊断[J].装备环境工程,2024,21(10):94-100. MA Jiteng,LYU Weimin,LI Gen,DAI Ziqin.Bearings Fault Diagnosis under Variable Operating Conditions Based on Invariant Risk Minimization[J].Equipment Environmental Engineering,2024,21(10):94-100. |
| 基于不变风险最小化的未知工况轴承故障诊断 |
| Bearings Fault Diagnosis under Variable Operating Conditions Based on Invariant Risk Minimization |
| 投稿时间：2024-06-21 修订日期：2024-08-13 |
| DOI：10.7643/issn.1672-9242.2024.10.012 |
| 中文关键词: 不变风险最小化 轴承 故障诊断 振动信号 深度学习 迁移学习中图分类号:TH165.3 文献标志码:A 文章编号:1672-9242(2024)10-0094-07 |
| 英文关键词:invariant risk minimization bearings fault diagnosis vibration signal deep learning transfer learning |
| 基金项目: |
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| Author | Institution |
| MA Jiteng | Naval Aviation University, Shandong Yantai, 264001, China |
| LYU Weimin | Naval Aviation University, Shandong Yantai, 264001, China |
| LI Gen | Naval Aviation University, Shandong Yantai, 264001, China |
| DAI Ziqin | Naval Aviation University, Shandong Yantai, 264001, China |
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| 中文摘要: |
| 目的 实现在未知工况下能够稳定识别轴承故障类型的智能故障诊断。方法 针对传统智能故障诊断方法模型泛化能力弱、领域适应等迁移学习方法数据依赖性强的问题，提出一种基于不变风险最小化的未知工况轴承故障诊断方法。利用多个可用工况的振动信号数据，经过数据增强技术进行预处理，并采用不变风险最小化策略来约束特征提取器和领域分类器，使其能够学习到在未知工况中不变的特征和知识。该方法能够有效提升模型在未知工况下的泛化能力，减少对特定工况数据的依赖。结果 实验证明，该方法在多个未知工况的诊断任务上实现了令人满意的诊断精度和鲁棒性。结论 在面对工况信息未知但故障特征相关的情况时，无需未知工况的对应数据，该方法使得深度模型可以更好地泛化并实现高精度轴承故障诊断。 |
| 英文摘要: |
| The work aims to achieve intelligent fault diagnosis that can stably identify bearing fault types under unknown working conditions. To address the problems of weak model generalization ability in traditional intelligent fault diagnosis methods and the strong data dependency of transfer learning methods such as domain adaptation, an unknown working condition bearing fault diagnosis method based on invariant risk minimization was proposed. The vibration signal data from multiple available working conditions were preprocessed through data augmentation techniques. The invariant risk minimization strategy was adopted to constrain the feature extractor and domain classifier, enabling them to learn features and knowledge that remain invariant across unknown working conditions. This method could effectively improve the generalization ability of the model under unknown working conditions and reduce the reliance on specific working condition data. Experiments demonstrated that this method achieved satisfactory diagnostic accuracy and robustness in diagnostic tasks under multiple unknown working conditions. In conclusion, when facing situations where the working condition information is unknown but fault characteristics are related, this method allows deep models to generalize better and achieve high-precision bearing fault diagnosis without requiring corresponding data from unknown working conditions. |
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