Integrating Trend Monitoring and Change Point Detection for Wind Turbine Blade Diagnostics: A Physics-Driven Evaluation of Erosion and Twist Faults

Robust condition monitoring of wind turbine blades is essential for reducing downtime and maintenance costs, particularly under variable operating conditions. While recent studies suggest that combining trend monitoring (TM) with change point detection (CPD) can improve diagnostic performance, it remains unclear whether such integration is beneficial for all fault types. This study experimentally evaluates the integration of TM and CPD using vibration data from a laboratory-scale wind turbine for two representative blade faults: leading-edge erosion and twist misalignment. For the erosion case, discrete wavelet transform (DWT) energy features exhibit a clear and persistent increase in mid-frequency content, with energy deviations of approximately 34–45% relative to the healthy state. However, Bayesian Online Change Point Detection (BOCPD) does not reveal distinct change points, indicating that CPD provides limited additional value for gradual, steady-state degradation. In contrast, for twist misalignment, the short-time Fast Fourier Transform (FFT) features reveal dynamic spectral redistribution, and CPD applied to spectral centroid trends produces a sharp, localized detection signature. These results demonstrate that integrating TM with CPD significantly enhances fault detectability for dynamic, instability-driven faults, while TM alone is sufficient for smooth, steady-state degradation. This study provides an evidence-based guideline for selectively integrating CPD into wind turbine blade condition monitoring systems based on fault physics.