上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (5): 561-568.doi: 10.16183/j.cnki.jsjtu.2023.375
• 新型电力系统与综合能源 • 下一篇
收稿日期:
2023-08-07
修回日期:
2023-11-29
接受日期:
2023-12-04
出版日期:
2025-05-28
发布日期:
2025-06-05
通讯作者:
贺 兴,副研究员,博士生导师;E-mail:hexing_hx@126.com.
作者简介:
潘美琪(1998—),硕士生,从事数字孪生和故障诊断研究.
基金资助:
Received:
2023-08-07
Revised:
2023-11-29
Accepted:
2023-12-04
Online:
2025-05-28
Published:
2025-06-05
摘要:
在工程实践中,风力机故障诊断面临训练故障与实际故障类别不同的情况,为实现对风力机未知故障的诊断,需要将训练过程中习得的故障特征信息迁移至未知故障中.不同于直接建立故障样本与故障类别间映射关系的传统方法,提出一种基于零样本学习的风力机故障诊断方法来完成故障特征迁移.通过描述每种故障的属性建立故障属性矩阵,将其嵌入故障样本空间与故障类别空间之中;并基于卷积神经网络建立故障属性学习器,基于欧氏距离建立故障分类器,形成从故障样本预测故障属性进而分类故障的诊断流程.最后通过与其他零样本学习方法的对比验证了所提故障诊断方法的有效性和优越性.
中图分类号:
潘美琪, 贺兴. 基于零样本学习的风力机故障诊断方法[J]. 上海交通大学学报, 2025, 59(5): 561-568.
PAN Meiqi, HE Xing. A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning[J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 561-568.
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