Damage Detection in Wind Turbine Blades Using Time-Frequency Analysis of Vibration Signals

被引：0

作者：

Fitzgerald, Breiffni ^{[1
]}

Arrigan, John ^{[1
]}

Basu, Biswajit ^{[1
]}

机构：

[1] Trinity Coll Dublin, Dublin, Ireland

来源：

2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年

关键词：

Wind turbine blades; Tower blade coupling; Short Time Fourier Transform;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The dynamic behavior of modern multi-Megawatt wind turbines has become an important design consideration. One of the major aspects related to the reliability of operation of the turbines concerns the safe and adequate performance of the blades. The aim of this paper is to develop a time-frequency based algorithm to detect damage in wind turbine blades from blade vibration signals. It is important that damage to blades is detected before they fail or cause the turbine to fail. A wind turbine model was developed for this paper. The parameters considered were the rotational speed of the blades and the stiffness of the blades and the nacelle. The model derived considers the structural dynamics of the turbine and includes the dynamic coupling between the blades and the tower. The algorithm developed uses a frequency tracking technique. Numerical simulations have been carried out to study the effectiveness of the algorithm.

引用

页数：5

共 50 条

[31] Monitoring conditions of ball bearings by vibration signals with time-frequency analysis
Silva, A.A.
Mucheroni, M.F.
Informacion Tecnologica, 2002, 13 (06): : 95 - 100
[32] Damage Sensitive Signals for the Assessment of the Conditions of Wind Turbine Rotor Blades Using Electromagnetic Waves
Al-Yasiri, Zainab Riyadh Shaker
Mutashar, Hayder Majid
Guerlebeck, Klaus
Lahmer, Tom
INFRASTRUCTURES, 2022, 7 (08)
[33] Damage detection of wind turbine blades by Bayesian multivariate cointegration
Xu, Mingqiang
Li, Jun
Wang, Shuqing
Yang, Ning
Hao, Hong
OCEAN ENGINEERING, 2022, 258
[34] Stationarity test of vibration signals with surrogate data and time-frequency analysis
Wang, Zuo-Cai
Wu, Feng
Ren, Wei-Xin
ADVANCES IN STRUCTURAL ENGINEERING, 2017, 20 (08) : 1143 - 1154
[35] Overview of Time-frequency Analysis Techniques in Vibration Signals of Rotating Machinery
Li, Hong
He, Qing
Zhang, Zhao
PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MECHANICS AND MECHANICAL ENGINEERING, 2014, 684 : 124 - 130
[36] Vibration Frequency of Wind Turbine Blades Based on Binocular Stereo Photography
Mamtimin, Geni
Liu, Huijie
Gulbahar, Tohti
Wu, Zhiqiang
Yakup, Ahmat
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2024, 44 (06): : 1112 - 1119
[37] Condition Monitoring and Damage Location of Wind Turbine Blades by Frequency Response Transmissibility Analysis
Yang, Wenxian
Lang, Ziqiang
Tian, Wenye
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (10) : 6558 - 6564
[38] Machine Learning Techniques for Damage Detection in Wind Turbine Blades
Tavares, Andre
Lopes, Bernardo
Di Lorenzo, Emilio
Cornelis, Bram
Peeters, Bart
Desmet, Wim
Gryllias, Konstantinos
EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 1, 2023, 253 : 176 - 189
[39] The analysis of ECG signals using time-frequency techniques
Tagluk, ME
English, MJ
PROCEEDINGS OF THE 19TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 19, PTS 1-6: MAGNIFICENT MILESTONES AND EMERGING OPPORTUNITIES IN MEDICAL ENGINEERING, 1997, 19 : 1320 - 1323
[40] Damage detection in RAPTOR telescope systems using time-frequency analysis methods
Antoniadou, I.
Worden, K.
Manson, G.
Dervilis, N.
Taylor, S. G.
Farrar, Charles R.
SMART DIAGNOSTICS V, 2014, 588 : 43 - +

← 1 2 3 4 5 →