摘要
为了能够有效的识别直驱式风电机组主轴轴承的故障信号,针对轴承故障所产生的调制多边频带成分在时频谱中较难辨识的特点;基于倒频谱的特点(1)对信号加权处理,突出了小周期信号,使信号的边频带成分得到凸显,(2)对信号取对数,将振源信号与传递信号分离。并通过实例对比分析进行验证,以时频域特征为辅助确定了其故障及故障的信号特征,倒频域方法诊断出故障发生的部位。该方法为风电机组轴承的故障检测提供了良好的方法,也为风电机组的在线监测提供了很好的借鉴。
In order to detect the fault signal of direct-drive wind turbines main shaft bearing, as bearing fault produces multilateral band of modulation, which is more difficult to identify in time and frequency domain, the Cepstrum had own characteristic ： （ 1 ）the signal was weighted, the small periodic signal was highlighted and the side-band of the signal was prominented; （2）the logarithm of signal was taken, the vibration source signal and the transfer signal is separated. And through the instance analysis that was wind turbine bearing, time and frequeney domain features determine its faults and fault signal characteristics, and cepstrum method determines the fault location. The method not only provided a good method for fault diagnosis of wind turbine bearings, but also provided a good reference for online monitoring of wind turbines.
出处
《机械设计与制造》
北大核心
2014年第7期265-267,共3页
Machinery Design & Manufacture
基金
山西省攻关项目资助(20110322003)
关键词
倒频谱
直驱
主轴轴承
边频带
故障诊断
Cepstrum
Direct-Drive
Main Shaft Bearing
Band of Modulation
Fault Diagnosis
