报告详情
A deep modeling approach based on time-frequency domain feature extraction
编号：24
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更新：2025-10-11 22:15:08 浏览：1次
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摘要
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.
关键词
Wind turbines; FFT; Feature Extractions; Deep Belief Network
报告人
meng jiao wang
Teaching Assistant Yanching Institute of Technology稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月12日 2025
初稿截稿日期
-
10月20日 2025
注册截止日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
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
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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