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In recent years, wind turbines have shown a maximization trend. However, most of the wind turbine blades operate in areas with a relatively poor natural environment. The stability, safety, and reliability of blade operation are facing many challenges. Therefore, it is of great significance to monitor the structural health of wind turbine blades to avoid the failure of wind turbine outages and reduce maintenance costs. This paper reviews the commonly observed types of damage and damage detection methods of wind turbine blades. First of all, a comprehensive summary of the common embryonic damage, leading edge erosion, micro-cracking, fiber defects, and coating defects damage. Secondly, three fault diagnosis methods of wind turbine blades, including nondestructive testing (NDT), supervisory control and data acquisition (SCADA), and vibration signal-based fault diagnosis, are introduced. The working principles, advantages, disadvantages, and development status of nondestructive testing methods are analyzed and summarized. Finally, the future development trend of wind turbine blade detection and diagnosis technology is discussed. This paper can guide the use of technical means in the actual detection of wind turbine blades. In addition, the research prospect of fault diagnosis technology can be understood.

With the increasing installed capacity of wind turbines, ensuring the safe operation of wind turbines is of great significance. However, the failure of wind turbines is still a severe problem, especially as blade damage can cause serious harm. To detect blade damage in time and prevent the accumulation of microdamage of blades evolving into severe injury, a damage dataset based on GH Bladed simulation of blade damage is proposed. Then, based on the wavelet packet analysis theory method, the MATLAB software can automatically analyze and extract the energy characteristics of the signal to identify the damage. Finally, the GH Bladed simulation software and MATLAB software are combined for fault diagnosis analysis. The results show that the proposed method based on GH Bladed to simulate blade damage and wavelet packet analysis can extract damage characteristics and identify single-unit damage, multiple-unit damage, and different degrees of damage. This method can quickly and effectively judge the damage to wind turbine blades; it provides a basis for further research on wind turbine blade damage fault diagnosis.

Onshore wind turbines are primarily installed in high-altitude areas with good wind energy resources. However, in winter, the blades are easy to ice, which will seriously impact their aerodynamic performance, as well as the power and service life of the wind turbine. Therefore, it is of great practical significance to predict wind turbine blade icing in advance and take measures to eliminate the adverse effects of icing. Along these lines, three approaches to supervisory control and data acquisition (SCADA) data feature selection were summarized in this work. The problems of imbalance between positive and negative sample datasets, the underutilization of SCADA data time series information, the scarcity of high-quality labeled data, and weak model generalization capabilities faced by data-driven approaches in wind turbine blade icing detection, were reviewed. Finally, some future trends in data-driven approaches were discussed. Our work provides guidance for the use of technical means in the actual detection of wind turbine blades. In addition, it also gives some insights to the further research of fault diagnosis technology.