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

Huiling Li, Cong Liu (), Qinjun Du, Qingtian Zeng, Jinglin Zhang, Georgios Theodoropoulo and Long Cheng
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Huiling Li: School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China
Cong Liu: NOVA Information Management School, Nova University of Lisbon, 1070-312 Lisbon, Portugal
Qinjun Du: School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China
Qingtian Zeng: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jinglin Zhang: School of Control Science and Engineering, Shandong University, Jinan 250100, China
Georgios Theodoropoulo: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Long Cheng: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 16, 1-17

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.

Keywords: wind-turbine maintenance process; next-event prediction; event log sampling; deep-learning model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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