摘要
对发电机传动轴承的异常振动特征的准确提取是实现机械运行状态检测和发电机动轴承疲劳损伤预测的基础。当前对发电机传动轴承的异常振动的特征提取采用功率谱密度特征提取方法,由于功率谱密度特征具有非高斯性,对交变温度下发电机传动轴承异常振动的跟随性能不好。提出一种基于Hilbert变换的发电机传动轴承的异常振动谱特征提取算法。建立一个多参量的发电机传动轴承动力学模型,进行轴承的振动分析模型构建,采用经验模态分解和Hilbert谱提取方法把发电机异常振动进行多分量分解,提取发电机传动轴承的异常振动的Hilbert谱特征,计算接触轴承所产生的轴承力的响应幅值和时间滞后值,实现对振动特征的横向、扭转的定位和检测。以此为基础优化传动系统的结构和动力学参数,进行齿轮的啮合异常修正。仿真结果表明,该方法稳定可靠、性能优越,提高了发电机传动轴承的状态监测和损伤预测能力。
The accurate extraction of the abnormal vibration characteristics of generator drive bearing is the basis of realizing the mechanical running state detection and prediction of the fatigue damage of motor bearing.At present,the feature extraction method of the abnormal vibration of generator drive bearing is obtained by using the method of power spectrum density feature extraction.Because of the characteristic of power spectral density,the following performance is not good for the abnormal vibration of generator bearing transmission bearing under the alternating temperature.An abnormal vibration spectrum feature extraction algorithm based on Hilbert transform for generator bearing is proposed.The dynamic model of a multi parameter generator is established,and the vibration analysis model of bearing is built.The abnormal vibration of generator is decomposed by empirical mode decomposition and Hilbert spectrum.The Hilbert spectrum of the abnormal vibration of generator is extracted.Based on the structure and dynamic parameters of the drive system,the gear mesh modification is carried out.Simulation results show that the method is stable,reliable,and performance is superior,and the state monitoring and damage prediction ability of generator is improved.
出处
《国外电子测量技术》
2016年第5期20-23,38,共5页
Foreign Electronic Measurement Technology
关键词
发电机
传动轴承
特征提取
损伤预测
generator
transmission bearing
feature extraction
damage prediction
