A carregar...
Publicação
Optimizing resource allocation for wind turbine maintenance through process and fault data fusion
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.62 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Wind turbine operation and maintenance enhance equipment reliability and help regulate electricity costs. Efficient maintenance resource allocation is essential for reducing maintenance expenses. However, existing maintenance resource allocation approaches face several challenges, including imbalanced distribution of human resources and inefficiencies in maintenance operations. To address these challenges, we propose a novel wind turbine maintenance resource allocation strategy by fusing process data and fault data in modern wind power systems. First, wind turbine maintenance process (WTMP) data are integrated with fault data to construct comprehensive fault-process data. Then, the Petri net-based WTMP resource model is discovered from the fault-process data using process mining techniques. Next, we propose a novel cost- and time-aware resource allocation strategy to assign the most suitable resources for each maintenance task. Finally, four deep learning models (LSTM, BiLSTM, GRU, and BiGRU) are built on top of the proposed allocation strategy to predict the time and cost of maintenance tasks. Based on the prediction results, an optimized allocation strategy is obtained and applied to the Petri net-based WTMP resource model. Experimental evaluation using real-life data from the Huangyi Wind Farm in Hebei Province, China, demonstrates that the proposed fusion-based approach effectively discovers an optimized WTMP resource model, while also reducing maintenance costs and improving maintenance efficiency. In addition, the BiGRU model combined with the proposed allocation strategy achieves the optimized resource allocation and the maintenance process time is reduced by up to 96%.
Descrição
Li, H., Liu, C., Du, Q., Zeng, Q., Liu, H., Wang, Q., & Cheng, L. (2026). Optimizing resource allocation for wind turbine maintenance through process and fault data fusion. Information Fusion, 126, Article 103678. https://doi.org/10.1016/j.inffus.2025.103678 --- This paper is supported by the National Natural Science Foundation of China (No. 62472264 and 52374221), the Natural Science Foundation of Shandong Province (ZR2025QA13 and ZR2022MF319).
Palavras-chave
Data fusion Deep learning model Process mining Resource allocation Wind turbine maintenance Software Signal Processing Information Systems Hardware and Architecture