Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring
Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring
复制标题
双变量经验模态分解及其对风力发电机状态监测的贡献
DOI：
10.1016/j.jsv.2011.02.027
复制
发表时间：
2011-07-18
影响因子：
4.7
通讯作者：
Crabtree, Christopher J.
中科院分区：
文献类型：
--
作者：
Yang, Wenxian;Court, Richard;Crabtree, Christopher J.
海上风力发电机的运行困难和恶劣环境要求更先进的状态监测技术，以确保海上风力发电机的高可用性。经验模态分解（EMD）已被证明是满足这一需求的一种有前途的技术。然而，EMD是针对一维信号开发的，无法执行信息融合功能，这对于得出可靠的状态监测结论至关重要。因此，二元经验模态分解（BEMD），本文研究，以评估它是否可以是一个更好的解决方案，风力涡轮机状态监测。所提出的技术在检测机器的早期故障的有效性进行了比较EMD和最近开发的基于小波的“能量跟踪”技术。实验结果表明，基于BEMD的方法比EMD方法更便于处理轴振动信号，在处理非平稳、非线性的风力涡轮机状态监测信号和检测早期机械和电气故障方面比EMD和小波分析方法更有效。(C)2011爱思唯尔有限公司版权所有。
Accessing difficulties and harsh environments require more advanced condition monitoring techniques to ensure the high availability of offshore wind turbines. Empirical mode decomposition (EMD) has been shown to be a promising technique for meeting this need. However, EMD was developed for one-dimensional signals, unable to carry out an information fusion function which is of importance to reach a reliable condition monitoring conclusion. Therefore, bivariate empirical mode decomposition (BEMD) is investigated in this paper to assess whether it could be a better solution for wind turbine condition monitoring. The effectiveness of the proposed technique in detecting machine incipient fault is compared with EMD and a recently developed wavelet-based 'energy tracking' technique. Experiments have shown that the proposed BEMD-based technique is more convenient than EMD for processing shaft vibration signals, and more powerful than EMD and wavelet-based techniques in terms of processing the non-stationary and nonlinear wind turbine condition monitoring signals and detecting incipient mechanical and electrical faults. (C) 2011 Elsevier Ltd. All rights reserved.