scholarly journals Monitoring and Damage Detection in Structural Parts of Wind Turbines

10.5772/21456 ◽  
2011 ◽  
Author(s):  
Andreas Friedmann ◽  
Dirk Mayer ◽  
Michael Koch ◽  
Thomas Siebel
Keyword(s):  
Damage Detection ◽  
Wind Turbines ◽  
Structural Parts

scholarly journals MEMS Multi Sensor Intelligent Damage Detection for Wind Turbines by Using IOT

2021 ◽  
Vol 1916 (1) ◽  
pp. 012045
Author(s):  
K M Majidha Fathima ◽  
R Sharan Raj ◽  
K Rahul Prasad ◽  
S Guru Balan
Keyword(s):  
Damage Detection ◽  
Wind Turbines

Detection of Damages in Mooring Lines of Spar Type Floating Offshore Wind Turbines Using Fuzzy Classification and Arma Parametric Modeling

2021 ◽  
pp. 2150111
Author(s):  
Mousa Rezaee ◽  
Reza Fathi ◽  
Vahid Jahangiri ◽  
Mir Mohammad Ettefagh ◽  
Aysan Jamalkia ◽  
...  
Keyword(s):  
Damage Detection ◽  
Success Rate ◽  
Wind Turbines ◽  
Parametric Modeling ◽  
Degree Of Freedom ◽  
Fuzzy Classification ◽  
Detection Methods ◽  
Measured Signal ◽  
Mooring Lines ◽  
Floating Offshore Wind Turbines

Floating wind turbines may encounter severe situations because of harsh environments. Higher cost of repair and maintenance of floating wind turbines have led researches to focus on damage detection methods that can prevent sudden failures. This paper presents an applicable method of damage detection and structural health monitoring for floating wind turbines based on the autoregressive moving average (ARMA) model and fuzzy classification. First, the dynamic model of a spar type floating wind turbine is constructed, by which the time responses of each degree of freedom of the system are acquired. With the system’s nonlinearity included, the intrinsic mode functions are obtained for the response signal. The Hilbert–Huang transform is applied and the appropriate measured signal for each degree of freedom is chosen for the ARMA modeling. In order to evaluate the proposed method, the ARMA parameters are first estimated for the undamaged condition then assumed damages are injected to the model and the ARMA parameters are once again estimated for the damaged condition. These parameters are considered as inputs for the fuzzy classification method. After training the system using the assumed damaged and undamaged conditions, the proposed method is simulated. Furthermore, the effect of measurement noise on the success rate is investigated. The results show that, in the presence of noise, the proposed method is able to identify the damage location and severity of mooring lines with acceptable success rate.

Structural Health Monitoring of Composite Material Typical of Wind Turbine Blades by Novelty Detection on Vibration Response

2012 ◽  
Vol 518 ◽  
pp. 319-327
Author(s):  
Nikolaos Dervilis ◽  
R. Barthorpe ◽  
Wieslaw Jerzy Staszewski ◽  
Keith Worden
Keyword(s):  
Composite Material ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Novelty Detection ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Structural Health

New generations of offshore wind turbines are playing a leading role in the energy arena. One of the target challenges is to achieve reliable Structural Health Monitoring (SHM) of the blades. Fault detection at the early stage is a vital issue for the structural and economical success of the large wind turbines. In this study, experimental measurements of Frequency Response Functions (FRFs) are used and identification of mode shapes and natural frequencies is accomplished via an LMS system. Novelty detection is introduced as a robust statistical method for low-level damage detection which has not yet been widely used in SHM of composite blades. Fault diagnosis of wind turbine blades is a challenge due to their composite material, dimensions, aerodynamic nature and environmental conditions. The novelty approach combined with vibration measurements introduces an online condition monitoring method. This paper presents the outcomes of a scheme for damage detection of carbon fibre material in which novelty detection approaches are applied to FRF measurements. The approach is demonstrated for a stiffened composite plate subject to incremental levels of impact damage.

MEMS Multisensor Intelligent Damage Detection for Wind Turbines

2015 ◽  
Vol 15 (3) ◽  
pp. 1437-1444 ◽  
Author(s):  
Maziar Moradi ◽  
Siva Sivoththaman
Keyword(s):  
Damage Detection ◽  
Wind Turbines

scholarly journals Study on Improvement of Lightning Damage Detection Model for Wind Turbine Blade

Machines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Takuto Matsui ◽  
Kazuo Yamamoto ◽  
Jun Ogata
Keyword(s):  
Damage Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Detection System ◽  
Turbine Blades ◽  
Lightning Strike ◽  
Wind Turbine Blades ◽  
Lightning Strikes ◽  
Detection Model ◽  
Secondary Damage

There have been many reports of damage to wind turbine blades caused by lightning strikes in Japan. In some of these cases, the blades struck by lightning continue to rotate, causing more serious secondary damage. To prevent such accidents, it is a requirement that a lightning detection system is installed on the wind turbine in areas where winter lightning occurs in Japan. This immediately stops the wind turbine if the system detects a lightning strike. Normally, these wind turbines are restarted after confirming soundness of the blade through visual inspection. However, it is often difficult to confirm the soundness of the blade visually for reasons such as bad weather. This process prolongs the time taken to restart, and it is one of the causes that reduces the availability of the wind turbines. In this research, we constructed a damage detection model for wind turbine blades using machine learning based on SCADA system data and, thereby, considered whether the technology automatically confirms the soundness of wind turbine blades.

Electro-Mechanical Impedance Technique for Structural Health Monitoring of Composite Panels

2012 ◽  
Vol 525-526 ◽  
pp. 569-572 ◽  
Author(s):  
Martin Schwankl ◽  
Z. Sharif-Khodaei ◽  
M.H. Aliabadi ◽  
Christian Weimer
Keyword(s):  
Numerical Modelling ◽  
Damage Detection ◽  
Health Monitoring ◽  
Short Range ◽  
Mechanical Impedance ◽  
Detection Algorithm ◽  
Experimental Result ◽  
Stiffened Panel ◽  
Impedance Technique ◽  
Structural Parts

Numerical modelling of EMI for damage detection has been presented in this paper. The PZT model is validated against the published experimental result for free disk and tied to the structure. The numerical modelling of the PZT patch will result in the admittance measure of the structure. The imaginary part of the admittance measure is used for developing a self-diagnostic sensor system. The real part of the admittance measure was used to develop a damage detection algorithm. Damage detection using EMI method was successfully applied to a simple composite disk and a stiffened panel. The EMI method is suitable for short range damage detection in structural parts with limited or no access.

Damage detection in grouted connections using electromechanical impedance spectroscopy

2018 ◽  
Vol 233 (3) ◽  
pp. 947-950 ◽  
Author(s):  
Jochen Moll
Keyword(s):  
Impedance Spectroscopy ◽  
Damage Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Oil And Gas ◽  
Offshore Wind ◽  
Wind Loading ◽  
Offshore Wind Turbine ◽  
Offshore Wind Turbines ◽  
Electromechanical Impedance

Grouted connections are structural joints formed by a cementitious grout cast between two concentric circular tubes. They are widely used in the offshore construction of oil and gas platforms, and for offshore wind turbines (monopiles and jackets). However, their application in offshore wind turbine installations can be critical due to the high bending moments coming from wind loading. Recently, it was found that grouted connections show limited performance in offshore wind turbine installations leading to settlements between the steel tubes and steel/grout debonding. Hence, structural health monitoring techniques for grouted connections are needed that ensure a safe and reliable operation of offshore wind turbines. This short communication describes the successful application of electromechanical impedance spectroscopy for damage detection in grouted connections.

scholarly journals Aspects of structural health and condition monitoring of offshore wind turbines

2015 ◽  
Vol 373 (2035) ◽  
pp. 20140075 ◽  
Author(s):  
I. Antoniadou ◽  
N. Dervilis ◽  
E. Papatheou ◽  
A. E. Maguire ◽  
K. Worden
Keyword(s):  
Signal Processing ◽  
Damage Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Successful Implementation ◽  
Offshore Wind Turbines ◽  
Structural Health ◽  
The Past

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

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