# Advances, Challenges, and Recommendations for Non-Destructive Testing Technologies for Wind Turbine Blade Damage: A Review of the Literature from the Past Decade

## TL;DR

This paper reviews non-destructive testing methods for wind turbine blades, highlighting recent advances and challenges in predictive maintenance.

## Contribution

The study systematically reviews and evaluates NDT and SHM technologies for wind turbine blades over the past decade.

## Key findings

- Data-driven approaches and machine learning improve fault classification and anomaly diagnosis in blade inspection.
- Robotic platforms like drones enhance rapid and comprehensive blade assessment.
- Key barriers include environmental noise, signal attenuation, and the gap between lab and field methods.

## Abstract

As critical components of wind energy systems, the structural integrity of wind turbine blades is directly tied to the operational safety and economic performance of wind turbines. With blade designs trending toward larger and more flexible structures and operating environments becoming increasingly harsh, maintenance strategies must urgently shift from reactive approaches to predictive maintenance paradigms. From an engineering application perspective, this study conducts a systematic and critical review of non-destructive testing (NDT) and structural health monitoring (SHM) technologies for wind turbine blades. Drawing on the literature published over the past decade, we examine the field applicability, limitations, and engineering challenges of core NDT techniques—including vision-based methods, acoustic approaches, vibration analysis, ultrasound, and infrared thermography. Particular emphasis is placed on the integration of data-driven approaches with engineering practice, evaluating the role of machine learning in fault classification and anomaly diagnosis, as well as the contributions of deep learning to automated defect detection in image and signal data. Moreover, this paper critically discusses the growing use of robotic inspection platforms, such as unmanned aerial vehicles and climbing robots, as multi-sensor carriers enabling rapid and comprehensive blade assessment. By comparatively analyzing detection performance, cost, and automation levels across technologies, we identify key engineering barriers, including environmental noise robustness, signal attenuation within complex blade structures, and the persistent gap between laboratory methods and field deployment. Finally, we outline forward-looking research directions, encompassing multi-modal sensor fusion, edge computing for real-time diagnostics, and the development of standardized SHM systems aimed at supporting full lifecycle blade management.