Sampling-Based Next-Event Prediction for Wind-Turbine Maintenance Processes

Abstract

Accurate and efficient next-event prediction in wind-turbine maintenance processes (WTMPs) is crucial for proactive resource planning and early fault detection. However, existing deep-learning-based prediction approaches often encounter performance challenges during the training phase, particularly when dealing with large-scale datasets. To address this challenge, this paper proposes a Sampling-based Next-event Prediction (SaNeP) approach for WTMPs. More specifically, a novel event log sampling technique is proposed to extract a representative sample from the original WTMP training log by quantifying the importance of individual traces. The trace prefixes of the sampled logs are then encoded using one-hot encoding and fed into six deep-learning models designed for next-event prediction. To demonstrate the effectiveness and applicability of the proposed approach, a real-life WTMP event log collected from the HuangYi wind farm in Hebei Province, China, is used to evaluate the prediction performance of various sampling techniques and ratios across six predictive models. Experimental results demonstrate that, at a 30% sampling ratio, SaNeP combined with the LSTM model achieves a 3.631-fold improvement in prediction efficiency and a 6.896% increase in prediction accuracy compared to other techniques.