# Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis
## 3 Results and Discussion
of fault diagnosis of the planetary gearbox of a wind turbine that comprised collecting a time domain vibration signal from the planetary gearbox as a sample to be diagnosed, these samples are normalized in amplitude, and the training results are validated and tested to obtain fault-diagnosis results. The present invention has the advantages of solving the problem of time-consuming and labor-intensive collection of real equipment data, reducing the reliance on traditional methods on a large amount of real equipment data, and improving diagnostic accuracy. As the sectors of wind energy grow, business economics will demand increasingly careful management of costs, especially as the operation and maintenance (O&M) costs of WTs account for about 25-30% of the overall energy generation cost or 75-90% of the investment costs [16]. In view of that, condition monitoring (CM), fault diagnosis (FD), and nondestructive testing (NDT) are currently considered crucial means to increase the reliability and availability of wind turbines. With that, many researchers have focused on CM and FD for different components of wind turbines, especially for evaluating wind turbine blade failures [16; 43]. One of the many ways to perform CM and FD is through vibration analysis. Vibration analysis can be used as an early damage-detection method in order to evaluate the health conditions of a structure [28]. The major types of sensors to capture vibration signals are displacement sensors, velocity sensors, and accelerometers [44; 45]. The frequency ranges from low-frequency, middle-frequency, and high-frequency ranges and is used in the corresponding vibration sensors. Damage to a wind turbine's structure changes its properties, e.g., mass, stiffness, and damping. In addition, it is also possible to observe changes in the mechanical properties by modifying the modal frequency, mode shape, and damping ratio. Blade damage detection can be mentioned as an example; when the structure is excited by external forces, such as wind flow or turbulence, the dynamic responses of the structure act as vibration signals. These signal responses can be extracted to identify defects using signal processing techniques [25]. [PERSON] and colleagues evaluated a rotor imbalance fault in wind turbines by collecting the vibration signal from an accelerometer that monitored the wind turbine drive train. They proposed a vibration model using a complex Morlet wavelet transform applied to rotor-imbalance detection and a health indicator that quantified the degree of rotor imbalance by the ratio of the vibration amplitude of the rotational frequency to the third harmonics. The result showed a successful detection and qualification of rotor imbalance caused by a blade crack in an on-site wind turbine [46]. [PERSON] et al. studied the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearboxes. Using an artificial neural network (ANN), they demonstrated the differences between the predicted and true values, and the results showed the increased sensitivity of the detection in two cases of gearbox output-shaft bearing failures, indicating a planet bearing failure, which had gone undetected in the previous data [47]. [PERSON] et al. developed a vibration-monitoring system to be used in wind turbine blades with the objective of estimating the deflection on the blade tip in a wind turbine tower. The monitoring system developed in this research is considered easy to install and can measure in real time. The experiments were performed with a simple blade model of a 300 W rotating wind turbine, and the signals obtained from the strain gauges were acquired by a sensor network and sent to a computer via wireless communication. The results showed that the estimation accuracy was acceptably high, concluding that their proposed system was practical [27].