# Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis
## 1 Introduction
Many of the already installed WTs are aging, driving the growing maintenance and repair market, along with the demand for the development of new maintenance and repair technologies. As the demand for wind power continues to grow at exponential rates, maintenance will be a permanent factor related to costs, and it can directly influence energy prices and the competitiveness of renewable energy [17; 18]. Maintenance (O&M) activities are a critical aspect of reliability. As a matter of fact, the global O&M market is projected to grow to USD 27.4 billion by the year 2025. Maintenance is one of the leading costs in the total expenditure of a wind farm project and, if not effective, can cause drastic losses in energy production due to downtime [19; 20; 21]. Different faults can affect a WT with different degrees of severity and with different financial impacts (related to the shutdown time caused by the fault). The most common failures associated with WTs are electrical, mechanical, structural, and cyber incidents. When it comes to the components, the most frequent faults involve the electrical system, the blade and pitch system, the sensors, and the hydraulic system [22]. The different failures in the components of a WT can lead to damage directly linked to the electrical system, decreasing the power generated by the WT and increasing the overload of different mechanical parts. This overload can lead to an abnormal vibration spectrum, overheating, and shortening the life of the components of the wind turbine, directly affecting the quality of the energy generated [16; 23]. These failures cause serious incidents, both to the workers involved in the operations and to the environment in which they are exposed; in addition to the loss of efficiency in energy production, one of the main consequences of the late diagnosis of these failures is electrical breakdown and fire in the structural components of the turbines. Due to the height of the turbines, these failures are extremely dangerous and difficult to extinguish while releasing toxic fumes and can cause secondary fires in their immediate environment; they can also lead to operator fatalities [24]. Taking all these factors into consideration, research into condition monitoring and failure diagnosis of WTs is increasing dramatically, and new fault-prediction techniques are needed to avoid downtime and increase plant reliability [25]. Monitoring and fault diagnosis based on vibration analysis is one of the most commonly applied technological tools in the industry for rotating equipment. Moreover, it is considered the method that covers the greatest possible number of failures. A challenge associated with monitoring using vibration analysis is the multiple frequency ranges, both low and high, of the different components of a WT. Usually, vibration techniques range from statistical techniques to techniques based on signal processing algorithms to extract diagnostic data [23; 26]. [PERSON] and colleagues placed accelerometers on the main components of a WT and used a vibration-based condition monitoring methodology using signal processing techniques, such as the temporal variations in vibrating signals, spectrum analyses by fast Fourier transforms, and envelope analysis through Hilbert transformation to search for potential faults. The results of the analysis helped find a failure in the generator's non-driving-end bearing, and the vibration characteristics were analyzed and reduced after the bearing was replaced [26]. [PERSON] et al. designed a blade-vibration monitoring system. The system used a strain gauge installed at the root of the blade to calculate and measure the deflection in real time according to the monitored stress. The results showed an acceptably high estimation accuracy and considered the system practical [27]. Another non-destructive evaluation technology used for detecting faults in wind turbines is thermography. Thermography technology is an advanced method based on thermal imaging that detects different temperatures on structural surfaces [28].