Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis

LIU Zhan; BAO Yanyang; LI Dazi

doi:10.12096/j.2096-4528.pgt.22178

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Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis

Power Generation and Environmental Protection | 更新时间：2025-06-06

- Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis
- “In the field of wind turbine fault diagnosis, experts have proposed a diagnostic method based on wide convolutional deep neural networks, which effectively extracts fault features through signal preprocessing and PCA selection of data channels, providing a solution for wind turbine fault diagnosis.”
- Power Generation Technology Vol. 44, Issue 6, Pages: 824-832(2023)
- 作者机构：
  
  1.北京能高普康测控技术有限公司，北京市丰台区100070
  2.北京化工大学自动化研究所，北京市朝阳区100029
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62273026);High-Tech Ship Research Project of Ministry of Industry and Information Technology(MC-202025-S02)
- DOI：10.12096/j.2096-4528.pgt.22178
  CLC： TK 83
- Received：29 March 2023，
  
  Published：31 December 2023
- 稿件说明：
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刘展,包琰洋,李大字.基于重采样降噪与主成分分析的宽卷积深度神经网络风机故障诊断方法[J].发电技术,2023,44(06):824-832.

LIU Zhan,BAO Yanyang,LI Dazi.Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis[J].Power Generation Technology,2023,44(06):824-832.
刘展,包琰洋,李大字.基于重采样降噪与主成分分析的宽卷积深度神经网络风机故障诊断方法[J].发电技术,2023,44(06):824-832. DOI： 10.12096/j.2096-4528.pgt.22178.

LIU Zhan,BAO Yanyang,LI Dazi.Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis[J].Power Generation Technology,2023,44(06):824-832. DOI： 10.12096/j.2096-4528.pgt.22178.

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Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis

DOI：10.12096/j.2096-4528.pgt.22178