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.

Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

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.

A major barrier to the acceptance of small wind turbines is that they are perceived to be noisy particularly when mounted on monopole towers rather than traditional guy-wired ones. This paper discusses an aspect of noise propagation that has not been studied previously: vibration of the tower. To start studying the tower’s behavior, twenty four accelerometers were attached in two orthogonal lines along the 10 m tower of Southwest Windpower Skystream 2.4 kW wind turbine located at the edge of the city of Calgary. About 15 minutes of data were recorded in order to extract natural frequencies and corresponding mode shapes while the turbine was in operating. Operational modal analysis (OMA), in which input loads are considered the ambient input, is conducted to identify dominant modes up to 100 Hz. This range covers the infrasound region (