报告详情
Few-Shot Health State Recognition of Wind Turbine Drivetrain Using Denoising Diffusion Probabilistic Model And Self-Taught Learning Network
编号：32
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更新：2025-06-15 10:14:24 浏览：27次
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报告时间：暂无持续时间
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摘要
Abstract—Few-shot samples and variable operating conditions have long been research challenges for intelligent health state recognition in industrial equipment. This paper proposes an intelligent health state recognition method based on a Denoising Diffusion Probabilistic Model (DDPM) embedded with an Inception-structured Self-taught Learning Network (ISLN). The proposed method first converts collected vibration signals into time-frequency representations using Synchrosqueezed Wavelet Transform (SWT). These time-frequency representations are then fed into the DDPM for sample augmentation, generating enhanced training data for the ISLN to perform health state recognition. The method is validated using a wind turbine drivetrain fault simulator. Experimental results demonstrate that the proposed method achieves the highest intelligent diagnosis accuracy in identifying various bearing and gear faults, outperforming other intelligent diagnosis models.
关键词
few-shot sample，,variable operating conditions,,denoising diffusion probabilistic model,Inception,self-taught learning
报告人
jun zhang
Student Shanghai University稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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