A State-of-the-Art Review of Structural Health Monitoring Techniques for Wind Turbine Blades

Abstract Wind turbine blades (WTBs) have increased in size and complexity, resulting in higher operational demands and maintenance costs. Damage to these blades can significantly reduce turbine performance, lifespan, and power generation, while increasing safety risks. Effective structural health monitoring (SHM) is therefore essential for early damage detection and failure prevention. This paper presents a comprehensive review of various SHM techniques for WTBs, categorizing each technique into sensing methods (data acquisition) and analysis methods (data processing and interpretation). The review also addresses the causes and types of blade damage, severity ratings along with corresponding maintenance actions, and fatigue-induced damage progression. Advanced approaches, including machine learning, signal processing, hybrid methods, and emerging techniques such as piezo-based active sensing, electromechanical impedance, and Lamb wave tomography, are also explored for their potential to enhance SHM capabilities. Additionally, commercially available SHM systems and inspection platforms, such as unmanned aerial vehicles, are reviewed to highlight practical applicability. The review covers strain-based methods, acoustic emission, vibration analysis, thermography, ultrasonic testing, radiography, machine vision, and electromagnetic techniques, highlighting their advantages, limitations, and future research directions for improving SHM for WTBs.