## 2 Related Works  
In [5], they focused on the NDT techniques applicable to Wind Turbine Condition Monitoring and Fault Diagnosis (CMFD) of wind turbine blades (WT-CMFD), but it did not discuss what blade failure modes or how they should be diagnosed by the NDT techniques [4]. In [17] a non-destructive testing was used to implement fault diagnosis for wind turbine blades. Ultrasonic scanning is one of the most favorite NDT techniques used in various industries [18; 19]. It can be accomplished to find out whether observed damages are present in a WT blade structure [20; 21]. Infrared thermography or IRT is the way to actually measure a surface heat distribution. It can be used to inspect the operational status of all blades based on measuring the temperature difference in the laminate and adhesive joints, which are the most critical points in their structures [22; 23]. X-rays are a type of radiation called electromagnetic waves. It can penetrate into a wide range of materials [20; 21; 24; 25; 26]. It is possible to reveal materials' structural variations by X-ray images so these can be deployed to detect defects and faults in wind turbine component structures. For example in [20], the authors demonstrated an effective radiographic system to inspect WT blades [20]. The sonic waves tap test can be deployed to detect a disbond between the main spar and the skin laminate for a blade [27]. The NDT techniques are strong enough to explore incipient defects and fault propagation monitoring. However, most of the time, these implementations are expensive. Furthermore, NDT approaches are usually offline and intrusive techniques. It means that NDTs require stopping the current operations of the wind turbine for further measurements and analysis [19]. In Figure 2, some signal processing and intelligent methods are depicted for WT-CMFD. In [28], a research was introduced to smartly detect surface cracks of wind turbine blade based on unmanned aerial vehicles (UAVs) video stream. They used Haar-like features to detect crack regions. They trained an extended cascading classifier with some base models such as Support Vector Machine (SVM), LogitBoost and Decision Tree (DT) to solve this detecting problem. A comparison was performed to evaluate the success rate of their algorithm for both identifi  
Figure 2: Signal processing and intelligent methods for Wind Turbine Condition Monitoring and Fault Diagnosis (WT-CMFD) and prognosis [19]. of cracks [28]. In [29], they introduced a system for wind turbine inspection based on a multi-copter. The project was defined as object recognition and tracking problems. They used the Hough line transform to implement the feature extraction module for a wind turbine. The hub positioning was handled by an algorithm designed to identify the three-point star similarity and was tracked using the Kalman filter [29]. On the other side, the WT is not a perfect technology and it is flawed by its special biological impact. For example, it may pose a serious threat to some bird species and also bats [30]. Bird migrations in particular seasons may increase the risk of collision with WTs. It is an unwanted event that also can influence wind turbine operation and impose financial losses. Therefore, it is very important to perform real-time Vision Inspection (VI) of WTs in wide areas to avoid any collision thread [31]. The majority of wind turbines are embedded with gear and statistical reports show that damages due to gearbox account for at least the 20% of their unavailability time [15]. In [15], they tried to effectively address a novel and accurate method for wind turbine gearboxes fault detection based on both peak values and RMS of vibration signals. They presented well in their study that the overall accuracy of each CM technique depends on the physics of failure. Their studies suggested that a technology that benefits multiple techniques is essential for a holistic and reliable health assessment of wind turbine components [15].