Presentation Details
Few-Shot Health State Recognition of Wind Turbine Drivetrain Using Denoising Diffusion Probabilistic Model And Self-Taught Learning Network
ID：32
View Protection：ATTENDEE
Updated Time：2025-06-15 10:14:24 Hits：203
Poster Presentation
Start Time：Pending（Asia/Shanghai）
Duration：Pending
Session：No Session »
No files
Abstract
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.
Keywords
few-shot sample，,variable operating conditions,,denoising diffusion probabilistic model,Inception,self-taught learning
Speaker
jun zhang
Student Shanghai UniversitySubmission Author
All Comments
Important Date
-
Conference Date
Aug 01
2025
toAug 04
2025
-
Aug 20 2025
Draft paper submission deadline
Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
Contact Information
- Mr. Yanruqiang
- ya******@xjtu.edu.cn
Previous Conferences
-
2025-08-01 China wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023-09-21 China Hefei
2023 International Conference on Equipment Intelligent Operation and Maintenance
Submit Comment