The failure of wind turbine blades is a major concern in the wind power industry due to the resulting high cost. It is, therefore, crucial to develop methods to monitor the integrity of wind turbine blades. Different methods are available to detect subsurface damage but most require close proximity between the sensor and the blade. Thermography, as a non-contact method, may avoid this problem. Both passive and active pulsed and step heating and cooling thermography techniques were investigated for different purposes. A section of a severely damaged blade and a small “plate” cut from the undamaged laminate section of the blade with holes of varying diameter and depth drilled from the rear to provide “known” defects were monitored. The raw thermal images captured by both active and passive thermography demonstrated that image processing was required to improve the quality of the thermal data. Different image processing algorithms were used to increase the thermal contrasts of subsurface defects in thermal images obtained by active thermography. A method called “Step Phase and Amplitude Thermography”, which applies a transform-based algorithm to step heating and cooling data was used. This method was also applied, for the first time, to the passive thermography results. The outcomes of the image processing on both active and passive thermography indicated that the techniques employed could considerably increase the quality of the images and the visibility of internal defects. The signal-to-noise ratio of raw and processed images was calculated to quantitatively show that image processing methods considerably improve the ratios.

The idea of indicative fault diagnosis based on measuring the wind turbine tower sound and vibration is presented. It had been reported by a wind farm operator that a major fault on the generator bearing causes shock and noise to be heard from the bottom of the wind turbine tower. The work in this paper was conceived to test whether tower top faults could be identified by taking simple measurements at the tower base. Two accelerometers were attached inside the wind turbine tower, and vibration data was collected while the wind turbine was in operation. Tower vibration signals were analyzed using Empirical Mode Decomposition and the outcomes were correlated with the vibration signals acquired directly from the generator bearings. It is shown that the generator bearing fault signatures were present in the vibrations from the tower. The results suggest that useful condition monitoring of nacelle components can be done even when there is no condition monitoring system installed on the generator bearings, as is often the case for older wind turbines. In the second part of the paper, acoustic measurements from a healthy and a faulty wind turbine are shown. The preliminary analysis suggests that the generator bearing fault increases the overall sound pressure level at the bottom of the tower, and is not buried in the background noise.

A major barrier to the acceptance of small wind turbines is that they are perceived to be noisy. This paper investigates an aspect of noise emission that has not been considered; vibration and noise generation from the tower. First, vibration measurements were made using accelerometers placed on the 10.2 m monopole tower of a Skystream 2.4 kW wind turbine, and natural frequencies and corresponding deflection shapes were calculated. Second, the results from the survey were used to verify the predictions of a finite element model of the tower structure. Lastly, the tower’s acoustic emission was simulated computationally, as it was not possible to measure it accurately. Most vibration energy occurred in the very low frequency band (≤10 Hz). It was found that wind itself can only excite the first two bending modes. On the other hand, emitted noise from the tower at large distances can be neglected, as close to the tower, the noise can reach 30 dB.

The dynamics of drilling processes presents chaotic and unsteady characteristics, which prevent deterministic description. Vibration signals obtained during the microdrilling process contain rich information reflecting tool and process conditions. Experiments described in this paper show that as drill wear develops and intensifies, the energy distribution of the vibration signal tends to shift towards the low-frequency range. Traditional frequency domain analysis through the fast Fourier transform is not able to capture such transitions with desirable accuracy since the process is highly non-stationary. We propose a new method that combines the discrete wavelet transform with statistical estimations of the signal energy distribution to extract features describing such energy shifts quantitatively. Through a multiresolution transformation, four feature parameters most sensitive to drill wear conditions are extracted. A tool wear index is proposed as a linear function of the extracted features, which also represents the severity of tool wear. The effectiveness of the proposed method is shown through a case study at the end.

In wind turbine blade modeling, the coupling between rotor rotational motion and blade vibration has not been thoroughly investigated. The inclusion of the coupling terms in the wind turbine dynamics equations helps us understand the phenomenon of rotor oscillation due to blade vibration and possibly diagnose faults. In this study, a dynamics model of a rotor-blade system for a horizontal axis wind turbine (HAWT), which describes the coupling terms between the blade elastic movement and rotor gross rotation, is developed. The model is developed by using Lagrange's approach and the finite-element method has been adopted to discretize the blade. This model captures two-way interactions between aerodynamic wind flow and structural response. On the aerodynamic side, both steady and unsteady wind flow conditions are considered. On the structural side, blades are considered to deflect in both flap and edge directions while the rotor is treated as a rigid body. The proposed model is cross-validated against a model developed in the simulation software fatigue, aerodynamics, structure, and turbulence (fast). The coupling effects are excluded during the comparison since fast does not include these terms. Once verified, we added coupling terms to our model to investigate the effects of blade vibration on rotor movement, which has direct influence on the generator behavior. It is illustrated that the inclusion of coupling effects can increase the sensitivity of blade fault detection methods. The proposed model can be used to investigate the effects of different terms as well as analyze fluid–structure interaction.