用计算学习法检测风机叶片故障，提升能源系统可靠性
A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability
- 对比多种模型，逻辑回归在故障模式识别中表现最优
- 聚类方法精度更高，更擅长捕捉数据内在特征
- 方法易落地，适合风电运维与能源设施监测
本文提出一种基于计算学习技术的风力发电机叶片故障检测新方法。研究评估了两种模型：第一种采用逻辑回归，在故障模式识别上优于神经网络、决策树和朴素贝叶斯；第二种利用聚类方法，在精度和数据分割方面表现更优，表明其更能捕捉数据底层特征。结果表明，聚类方法在揭示数据结构方面优于监督学习。该方法为风机叶片早期故障检测提供新思路，强调融合不同计算学习技术可提升系统可靠性。使用Orange Data Mining等易用工具，凸显其在风电领域的实际应用价值。未来工作将整合两类方法以进一步提升检测精度，并拓展至其他关键能源基础设施部件。
原文摘要 · Abstract (English)
This paper presents a novel methodology for detecting faults in wind turbine blades using com-putational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure.
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