Joshuva A, Sugumaran V
School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur-Kelambakkam Road, Chennai 600127, India.
ISA Trans. 2017 Mar;67:160-172. doi: 10.1016/j.isatra.2017.02.002. Epub 2017 Feb 8.
Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade.
风能是自然界中重要的可再生能源资源之一。由于技术发展带来的可靠性以及相对较低的成本，它是能源生产的主要资源之一。利用旋转叶片可将风能转化为电能。由于环境条件和大型结构，叶片会受到各种振动力的作用，这可能会对叶片造成损坏。这会导致能源生产中的责任问题以及涡轮机停机。当使用结构健康状态监测对叶片进行连续诊断时，停机时间可以减少。这些被视为一个模式识别问题，它由三个阶段组成，即特征提取、特征选择和特征分类。在本研究中，从振动信号中提取统计特征，使用J48决策树算法进行特征选择，并使用最佳优先树算法和功能树算法进行特征分类。针对风力涡轮机叶片的故障诊断提出了更好的算法。
