Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion and cracks. However, a key gap remains in integrating these methods into a unified framework for fault prediction, which could offer a more comprehensive solution for diagnosing faults. This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. Open-source vibration data collected from an experimental setup (where a small wind turbine with an artificially eroded and cracked blade was tested) were utilized. The time- and frequency-domain features were extracted and analyzed using various machine learning algorithms. It was found that erosion and crack faults have unique time- and frequency-domain features. The crack fault introduces an amplitude modulation in the vibration time wave, which produces sidebands around the fundamental frequency in the frequency domain. However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time- and frequency-fault features enhances the performance of the machine learning algorithms. This study further illustrates that even though some machine learning algorithms provide similar high classification accuracy, they might differ in quantifying error Types I, II, and, III, which is extremely important for maintenance engineers, as it might lead to undetected fault events and false alarm events.

Remaining Useful Life (RUL) forecasting models are essential to enable predictive maintenance strategies. However, selecting the most appropriate model based solely on conventional accuracy metrics may be insufficient for practical decision making, where an adequate prediction horizon is required to plan maintenance activities. This study investigates the impact of prediction horizon on model performance and its implications for maintenance decision making. A multi-horizon evaluation approach is applied to assess model accuracy across different predictive horizons. The results show the fluctuation of accuracy and prediction error over different prediction horizons. Across both datasets, predictive accuracy was generally lowest at the long horizon (11.64–86.62%), remained variable at the medium horizon (18.13–82.04%), and was highest at the short horizon (30.29–98.25%). The results demonstrate that model performance varies significantly with the prediction horizon, highlighting a trade-off between prediction accuracy and the time available for operational planning. These findings emphasize that models with high short-term accuracy may not necessarily support effective maintenance decisions if sufficient lead time is not provided. The findings show how prediction horizon considerations shall be integrated into a risk-based evaluation framework, in which model performance is interpreted in relation to the operational consequences of prediction errors. A complete risk-based predictive maintenance framework is proposed to support a shift toward comprehensive, risk-based evaluation as a prerequisite for reliable and effective RUL prediction in predictive maintenance systems.