Naturally Damaged Wind Turbine Blade Bearing Fault Detection Using Novel Iterative Nonlinear Filter and Morphological Analysis
Naturally Damaged Wind Turbine Blade Bearing Fault Detection Using Novel Iterative Nonlinear Filter and Morphological Analysis
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DOI：
10.1109/tie.2019.2949522
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发表时间：
2020-10
影响因子：
7.7
通讯作者：
Zepeng Liu;Long Zhang
中科院分区：
文献类型：
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作者：
Zepeng Liu;Long Zhang
风力涡轮机叶片轴承是使叶片变桨的关键部件，其优化电能输出并停止风力涡轮机以进行保护。叶片轴承故障会导致涡轮机失去控制甚至发生故障。然而，由于转速非常低（通常小于5 r/min）和旋转角度有限（小于100 °），叶片轴承只能产生微弱且有限的运行状态数据，这使得状态监测和故障诊断非常具有挑战性，特别是对于自然损坏的情况。本文对一个在真实的风电场运行超过15年的大型叶片轴承的自然损伤进行了研究。提出了一种迭代非线性滤波器，用于滤除强噪声，提取微弱故障振动特征。然后，基于形态变换的包络方法应用于轴承故障的频域诊断。诊断结果表明，该方法可以作为诊断极低速叶片轴承故障的有效工具，上级一些传统的轴承故障诊断方法。
Wind turbine blade bearings are pivotal components to pitch blades, which optimize electrical energy output and stop wind turbines for protection. Blade bearing failure can cause the turbine to lose control or even break down. However, due to the very slow rotation speeds (often less than 5 r/min) and limited rotation angles (less than 100$\rm ^{o}$), blade bearings can only produce weak and limited operating condition data, which makes condition monitoring and fault diagnosis very challenging, in particular for naturally damaged conditions. In this article, a naturally damaged large-scale blade bearing, which was in operation on a real wind farm for over 15 years, is investigated. An iterative nonlinear filter is proposed to remove heavy noise and extract weak fault vibration features. Then, the morphological transform-based envelope method is applied to diagnose the bearing fault in the frequency domain. The diagnostic results show that the proposed method can be an effective tool for diagnosing very slow speed blade bearings and is superior to some conventional bearing fault diagnosis methods.