Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method
Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method
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DOI：
10.1016/j.renene.2019.06.094
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发表时间：
2020-02-01
期刊：
影响因子：
8.7
通讯作者：
Carrasco, Joaquin
中科院分区：
文献类型：
--
作者：
Liu, Zepeng;Zhang, Long;Carrasco, Joaquin
叶片轴承，也称为变桨轴承，是风力涡轮机的接头部件，其可以以期望的角度缓慢地使叶片变桨以优化电能输出。叶片轴承的故障会严重降低发电量，因此叶片轴承故障诊断对于防止昂贵的维修和意外故障至关重要。然而，诊断低速叶片轴承的主要困难是微弱的故障振动信号被许多噪声干扰掩盖，有效的振动数据非常有限。针对这些问题，本文首先研究了一个在风电场运行超过15年的大型低速叶片轴承的自然损坏。通过两个案例研究，分别采集了手动旋转和电机驱动两种工况下的振动数据。然后采用经验小波阈值法去除强噪声，提取微弱故障信号。诊断结果表明，该方法可以作为大型风力涡轮机叶片轴承自然损伤诊断的有效工具。(C)2019爱思唯尔有限公司版权所有。
Blade bearings, also termed pitch bearings, are joint components of wind turbines, which can slowly pitch blades at desired angles to optimize electrical energy output. The failure of blade bearings can heavily reduce energy production, so blade bearing fault diagnosis is vitally important to prevent costly repair and unexpected failure. However, the main difficulties in diagnosing low-speed blade bearings are that the weak fault vibration signals are masked by many noise disturbances and the effective vibration data is very limited. To address these problems, this paper firstly deals with a naturally damaged large-scale and low-speed blade bearing which was in operation on a wind farm for over 15 years. Two case studies are conducted to collect the vibration data under the manual rotation condition and the motor driving condition. Then, a method called the empirical wavelet thresholding is applied to remove heavy noise and extract weak fault signals. The diagnostic results show that the proposed method can be an effective tool to diagnose naturally damaged large-scale wind turbine blade bearings. (C) 2019 Elsevier Ltd. All rights reserved.