Long distance laser ultrasonic propagation imaging system for damage visualization (original) (raw)

DETECTION AND LOCALIZATION OF TRAILING EDGE DISBOND IN A LARGE WIND TURBINE BLADE

Efficient wind energy harvesting becomes more and more important as a consequence of the increasing interest in renewable energy in the European Union [1]. This leads to growing sizes of wind turbines (WTs), and with it, larger WT blades (WTBs). The structural designs of these WTBs are created to optimize the potential energy output, where low mass is a key requirement. However, high flexibilities and lower buckling capacities are further results of these developments [2], thus certain damage scenarios become significant. Intelligently designed structural health monitoring (SHM) systems can help to reduce the associated operations and maintenance costs. Even though, several techniques are already developed for structural damage detection (SDD) in WTBs, the majority of these methods is not suitable for in-service measurements. This paper presents a SDD and structural damage localization (SDL) method based on the partial autocorrelation function (PACF) of vibration responses. The approach is applied to a numerical model of a large WTB, where the acceleration responses are obtained from transient dynamic simulations with a simplified aerodynamic loading approach. The novel damage sensitive feature (DSF) is developed as the Mahalanobis distance between a baseline and current vector of PACF coefficients. First, numerical modal analysis of the finite element (FE) WTB model is performed in order to estimate the effect of a disbonding damage scenario on the vibration characteristics. Second, the behaviour of the PACF for time series of the healthy system is discussed. Third, the SDD results on the basis of statistical hypothesis testing are assessed for two selected sensor locations and increasing damage extents. Finally, the performance of the proposed DSF with respect to SDL is illustrated for multiple locations on the WTB’s surface. This study demonstrated the efficiency of a DSF based on the PACF for SDD and SDL, which is promising for future developments of vibration-based SHM techniques in WTBs.

Structural health monitoring techniques for wind turbine blades

Journal of Wind Engineering and Industrial Aerodynamics, 2000

Wind turbine blades are made of "berglass material to be cost e!ective, but they can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. It is important to detect the damage before the blade fails catastrophically which could destroy the entire wind turbine. In this paper, four di!erent algorithms are tested for detecting damage on wind turbine blades. These are the transmittance function, resonant comparison, operational de#ection shape, and wave propagation methods. The methods are all based on measuring the vibration response of the blade when it is excited using piezoceramic actuator patches bonded to the blade. The vibration response of the blade is measured using either piezoceramic sensor patches bonded to the blade, or a scanning laser doppler vibrometer. The sensitivity of the techniques to detect a reversible damage simulated by a steel plate clamped to a section of a wind turbine blade is compared in this paper.

Damage detection in wind turbine blades using time-frequency analysis of vibration signals

Facing the climate change the use of renewable energies gains in importance. Especially the wind energy branch grows very fast. Bigger and more powerful wind mills will be built in the next decades and the safety of the mills will play a major role. Wind turbines are treated as buildings and therefore have to be inspected at regular intervals. Especially the turbine blades are highly stressed during operation and a blade breakdown can cause a big economic damage.

Damage detection on a wind turbine blade section

1999

A scanning laser vibrometer and piezoceramic actuators are used for detecting damage on a section of a wind turbine blade. The laser is a noncontact sensor that can measure vibration at a large number of points on a structure over a wide frequency range. Piezoceramic patches are used to generate the vibration without mass-loading the structure. Three different methods are used for detecting damage. The methods are based on changes in transmittance functions, frequency response functions, and operational deflection shapes. Damage simulated by a steel plate clamped to the blade was detected by the three techniques.

Structural health monitoring of critical zones of small wind turbine blades for domestic users

IOP Conference Series: Materials Science and Engineering, 2019

The most important part of wind turbine is the blade that must be tested during fabrication and functioning when can be damaged by moisture absorption, fatigue, wind guests or lightning strikes. The fibers can also appear and develop under moderated loads or cracks and delaminating due to the low energy impacts etc. The aim of this paper is to identify the modal behavior of small wind turbine blade in terms of the signal convergence, modal evolutions and frequency spectra. There are numerous methods for monitoring and detecting structural damage of the wind blades, but in this study it was used the impact hammer method. The results emphasized the natural frequencies, the modal shape of structure and the signal coherence captured in different points.

An acoustic-array based structural health monitoring technique for wind turbine blades

Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, 2015

This paper proposes a non-contact measurement technique for health monitoring of wind turbine blades using acoustic beamforming techniques. The technique works by mounting an audio speaker inside a wind turbine blade and observing the sound radiated from the blade to identify damage within the structure. The main hypothesis for the structural damage detection is that the structural damage (cracks, edge splits, holes etc.) on the surface of a composite wind turbine blade results in changes in the sound radiation characteristics of the structure. Preliminary measurements were carried out on two separate test specimens, namely a composite box and a section of a wind turbine blade to validate the methodology. The rectangular shaped composite box and the turbine blade contained holes with different dimensions and line cracks. An acoustic microphone array with 62 microphones was used to measure the sound radiation from both structures when the speaker was located inside the box and also inside the blade segment. A phased array beamforming technique and CLEAN-based subtraction of point spread function from a reference (CLSPR) were employed to locate the different damage types on both the composite box and the wind turbine blade. The same experiment was repeated by using a commercially available 48-channel acoustic ring array to compare the test results. It was shown that both the acoustic beamforming and the CLSPR techniques can be used to identify the damage in the test structures with sufficiently high fidelity.

Using Wind Turbine Noise to Inspect Blade Damage through Portable Device

2019

Maintenance and repair of wind turbine components are important in the wind power industry. The wind turbine blades are damaged gradually because of longterm operation in severe weather conditions, especially the typhoon season in Taiwan. Wind farm operators still rely on the in-situ technician’s visual and auditory judgement to detect the wind turbine blade health condition. The traditional detection method by the human sense and subjective judge is inefficiency and inaccuracy. This paper is intended to provide the blade fault inspection method by using the wind turbine noise and to conduct on-site inspection of wind farm through designed portable devices. The advantage of this device is that it can inspect wind turbine blade during running operation. The routine inspection work can keep tracking the condition of each blade and the records furthermore could be used for maintain and repair in advance. It is expected that it will help improve the operational efficiency of Taiwan'...

Acoustic emission monitoring of wind turbine blades

Proceedings of SPIE, 2015

Damage to wind turbine blades can, if left uncorrected, evolve into catastrophic failures resulting in high costs and significant losses for the operator. Detection of damage, especially in real time, has the potential to mitigate the losses associated with such catastrophic failure. To address this need various forms of online monitoring are being investigated, including acoustic emission detection. In this paper, pencil lead breaks are used as a standard reference source and tests are performed on unidirectional glass-fiber-reinforced-polymer plates. The mechanical pencil break is used to simulate an acoustic emission (AE) that generates elastic waves in the plate. Piezoelectric sensors and a data acquisition system are used to detect and record the signals. The expected dispersion curves generated for Lamb waves in plates are calculated, and the Gabor wavelet transform is used to provide dispersion curves based on experimental data. AE sources using an aluminum plate are used as a reference case for the experimental system and data processing validation. The analysis of the composite material provides information concerning the wave speed, modes, and attenuation of the waveform, which can be used to estimate maximum AE event-receiver separation, in a particular geometry and materials combination. The foundational data provided in this paper help to guide improvements in online structural health monitoring of wind turbine blades using acoustic emission.

Damage detection of wind turbine system based on signal processing approach: a critical review

Clean Technologies and Environmental Policy, 2021

Abstract Numerous damage detection methods have been discovered to provide an early warning at the earliest possible stage against structural damage or any type of abnormality in the wind turbine system. In this paper, a comprehensive literature review is carried out in the field of damage detection for wind turbine systems. Several modern signal processing techniques including time-domain and frequency-domain analysis, joint time–frequency methods, entropy-based damage detection, supervisory control and data acquisition (SCADA), and machine learning approaches are all emphasized, and how to estimate the damage in wind turbine system by utilizing these various approaches is discussed. It is concluded that each of these methods offers its own unique merits and shortcomings in detecting certain types of damage with various levels of complexity. This research paper is aimed to inform the readers and experts about the damage detection techniques of the wind turbine system and fault diagnosis with various advanced signal processing methods. Graphical abstract