Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals
Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals
复制标题
使用实测振动信号通过经验小波变换进行风力涡轮机发电机轴承故障诊断
DOI：
10.1016/j.renene.2015.12.010
复制
发表时间：
2016-04-01
期刊：
影响因子：
8.7
通讯作者：
Chen, Xuefeng
中科院分区：
文献类型：
--
作者：
Chen, Jinglong;Pan, Jun;Chen, Xuefeng
风力涡轮机状态监测与故障诊断系统（CMFDS）的实现对降低非计划故障具有重要意义。发电机是涡轮机的重要组成部分，而发电机轴承故障识别一直是人们关注的焦点。然而，弱故障和复合故障的非平稳振动信号以及大量的背景噪声在许多情况下使这项任务具有挑战性。因此，有效的信号处理方法对CMFDS的准确诊断至关重要。经验小波变换（EWT）作为一种新的信号处理方法，通过将信号分解为正交基下的单分量，提取信号固有的调制信息，为机械故障诊断提供了有力的工具。此外，为了避免强噪声导致的内部模式识别不准确，在EWT前采用数据驱动阈值的小波空间邻域系数去噪，以提高信噪比。通过两个实验案例验证了该方法在弱故障和复合故障诊断中的有效性。最后，将该方法应用于风电场涡轮机发电机轴承故障特征识别。(C)2015爱思唯尔有限公司版权所有。
The implementation of condition monitoring and fault diagnosis system (CMFDS) on wind turbine is significant to lower the unscheduled breakdown. Generator is one of the most important components in wind turbine, and generator bearing fault identification always draws lots of attention. However, non stationary vibration signal of weak fault and compound fault with a large amount of background noise makes this task challenging in many cases. So, effective signal processing method is essential in the accurate diagnosis step of CMFDS. As a novel signal processing method, empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis. Moreover, in order to avoid the inaccurate identification the internal modes caused by the heavy noise, wavelet spatial neighboring coefficient denoising with data-driven threshold is applied to increase Signal to Noise Ratio (SNR) before EWT. The effectiveness of the proposed technique on weak fault and compound fault diagnosis is first validated by two experimental cases. Finally, the proposed method has been applied to identify fault feature of generator bearing on wind turbine in wind farm successfully. (C) 2015 Elsevier Ltd. All rights reserved.