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A deep modeling approach based on time-frequency domain feature extraction
ID：163 View Protection：ATTENDEE Updated Time：2025-11-03 11:39:25 Hits：272 Poster Presentation

Start Time：2025-11-09 09:03（Asia/Shanghai）

Duration：1min

Session：P Poster presentation » P66.AI-driven technology

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Abstract
Aiming at the problem of difficulty in extracting fault features of wind turbines under complex operating conditions, this study introduces a method for identifying wind turbine bearing faults based on vibration signals, extracting statistical features in the time domain, then performing a Fast Fourier Transform (FFT) on the original signal, and extracting the frequency domain features as well as statistical features after the FFT. The main features in the time-frequency domain features are then selected using chi-square test. In this study, deep confidence neural network (DBN) is used to classify the bearing faults. Finally, a comparative study is carried out by comparing the classification results with those of Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and the results show that the recognition accuracy of the method proposed in this study is 99.8%, which has a higher classification performance.
 
Keywords
Wind turbines; FFT; Feature Extractions; Deep Belief Network
Speaker
meng jiao wang
Yanching Institute of Technology

Submission Author
meng jiao wang Yanching Institute of Technology
Junfang Zhang Yanching Institute of Technology
Dongdong Zhao Yanching Institute of Technology
Xiangjie Wang Yanching Institute of Technology
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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