基于EEMD和能量算子的风机轴承故障诊断  

Fault Diagnosis of Wind Turbine Rolling Bearings Based on EEMD and Energy Operator Demodulation

作　　者：齐咏生[1,2] 张二宁 高胜利 高学金 王普[3,2] 

机构地区：[1]内蒙古工业大学电力学院,呼和浩特010000 [2]城市轨道交通北京实验室,北京100124 [3]北京工业大学电控学院,北京100124

出　　处：《控制工程》2017年第12期2450-2455,共6页Control Engineering of China

基　　金：国家自然科学基金项目(61174109,61364009);内蒙古自然科学基金(2015MS0615);内蒙古工业大学校重点科研项目(X201424)

摘　　要：针对风机轴承振动信号的非线性、非稳定性和能量算子对信号单分量的要求,提出一种基于聚合经验模态分解(EEMD)和Teager能量算子解调的方法,提取信号的瞬时频率和包络信号,通过与采集的正常信号进行比对,从而诊断风机轴承是否有故障,之后再通过捕捉频谱中突出的幅值信息,进而确定故障的原因。该方法能有效克服实际风机故障库难于获取和建立的问题,实现风机轴承故障的在线监测与诊断。最后,将该算法应用于实验室平台的测试信号和真实风机的故障信号,验证了该算法的有效性和实用性。Due to the nonlinearity and instability of the wind turbine signal and the requirements of energy operators to the single of signal-component, this paper presents a demodulation method based on ensemble empirical mode decomposition （EEMD） and Teager energy operator, through extracting instantaneous frequency and amplitude in the envelope and comparing with the normal signals to diagnose the bearing fault, then capturing the prominent amplitude information of the spectrum to confirm the reason of the fault. The method can effectively overcome the difficulty of obtaining the actual wind turbine fault library, and can realize on-line monitoring and fault diagnosis. In the end, this method is applied to the test signal and physical fault signals, the results verify the availability and practicability of the method.

关 键 词：风机故障诊断 风机故障监测 聚合经验模态分解 TEAGER能量算子 

分 类 号：TN911.72[电子电信—通信与信息系统]