# Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis
## 3 Results and Discussion
Thermography technology is an advanced method that uses thermal imaging to detect the temperature differences of structural surfaces [22]. The technique uses an infrared (IR) camera acting as a sensor and can inspect local damage or global damage, depending on the resolution of the camera, to access a single point or a full structure [48]. Material damages on the WT can be measured using temperature gradients in a non-destructive way, and the detected damaged part will present with a higher temperature than the normal part [49]. The use of thermography has some advantages, such as the availability for full field measurements, suitability for detecting damages caused by fatigue and delamination, capability for visual interpretation, and a short inspection interval. Inspection techniques can be split into two categories: active and passive. In active thermography, different heating sources are employed to heat the object, which makes the technique less frequently used for operating wind turbines, while passive thermography utilizes solar radiation to heat a blade (usually around sunrise) or to cool it at sunset [15]. This method has been widely used to detect the subsurface defects of different materials, including metals, composites, and concrete [50; 51; 52]. [PERSON] and colleagues assessed a continuous line laser scanning thermography (CLLST) system to remotely inspect internal delamination defects in wind turbine blades. In order to evaluate the feasibility of the CLLST system, both laboratory and full-scale tests were conducted using a carbon-fiber-reinforced polymer (CFRP) plate, a glass-fiber-reinforced polymer (GFRP) 10 kW wind turbine blade, and a 3 MW GFRP wind turbine blade. The results demonstrated that the 10 mm diameter internal delamination located 1 mm below the blade surface was successfully detected even at a 10 m distance from the target blade with a laser scan speed of 2 mm/s. [53]. [PERSON] et al. used thermography to detect potential subsurface defects or damage to offshore wind farms. It was performed from remote distances by a data processing algorithm, which differs from the ordinary method of thermographic analysis using thermal photographic images. The results showed the detection of potential subsurface defects within rotating rotor blades from greater distances, such as from the ground, aircraft, or vessels [54]. [PERSON] et al. employed IR-thermography-based detection to assess turbulent flow separation in wind turbine rotor blades during operation. The measurement approaches measured the surface temperature response to unsteady flow conditions and enabled unambiguous detection of flow separation via temperature fluctuation maxima in flow transition regions, as well as increasing temperature fluctuations within the separated flow region [55]. As WTs are exposed to harsh environmental conditions, such as rain, snow, and airborne particles, such as snow, rain, and ice, multiple faults can occur at the same time. Acknowleding that each failure detection method has its own limitations, new investigations are seeking to combine different failure detection techniques to provide more accurate and detailed information. As an example, [PERSON] et al. [56] and [PERSON] et al. [57] united the techniques that use acoustic and vibration signals for gearbox fault diagnosis, and the results proved the superior performance of multi-sensor fusion compared with single-sensor-based methods. In recent decades, artificial intelligence, such as machine learning, has been investigated for optimal control in industrial applications and is considered to be a promising approach to increasing system efficiency by including probability functions and nonlinear modeling into relevant signal processing methods. [PERSON] et al. proposed a deep-learning-based model called Multi-Resolution and Multi-Sensor Fusion Network (MRSFN) to evaluate motor fault diagnosis through the multi-scale analysis of motor vibrations and stator current signals.