With the high demand gas turbines are pushed to operate under extreme conditions. This leads to a range of failures in turbine blades, which include but not limited to thermal barrier coating degradation, oxidation, cracking, erosion, and thermal fatigue. These failures can progress to catastrophic outcomes such as blade fragmentation. Early detection is essential for ensuring safety, reliability, and efficiency. This review synthesizes the current understanding of turbine blade failure modes and evaluates the effectiveness of available monitoring techniques, including blade tip timing (BTT), vibrational monitoring, temperature monitoring, ultrasound, acoustic emission (AE), pressure-based, and performance monitoring methods. Each technique offers unique strengths, such as BTT’s precision in detecting early-stage failures and ultrasound’s capability for subsurface crack detection. However, ultrasound faces accessibility challenges in operational turbines, while AE monitoring is hindered by signal noise and limited damage localization. Pressure-based approaches and performance monitoring provide valuable system-wide insights but lack sensitivity for early damage detection. Despite significant advancements, no single technique offers a comprehensive solution. This review highlights the need for integrated diagnostic approaches that combine multiple techniques to address these limitations and improve turbine health monitoring. Key research gaps are identified, particularly in real-time data integration and the development of more robust, adaptive monitoring frameworks.
Almomani et al. (2026) studied this question.