摘要
针对风电齿轮箱多级传动导致的振动耦合调制问题,提出了一种考虑级间调幅调频的齿圈故障新模型,并通过参数辨识技术将其应用于齿圈故障监测。某级齿圈故障特征频率会以调幅、调频方式,调制不同轮系的啮合频率,呈现出耦合调制现象,本文针对该特殊调制规律,建立了两级齿圈故障下的振动信号耦合调制模型。在此基础上,提出基于局部均值分解和列文伯格-马夸尔特算法的参数辨识技术,确定故障辨识模型的调幅系数,进而可方便地构建出状态指标,以达到齿圈故障监测的目的。用现场数据进行验证,结果表明,新模型比传统模型描述振动信号更全面;借助参数辨识技术构建的指标能定位故障齿轮。
To address the vibration coupling modulation problem caused by the multi-stage transmission of the wind turbine gearbox,a new ring gear fault model considering the inter-stage amplitude modulation and frequency modulation is proposed.It is applied to the monitor ring gear by the parameter identification technology.The fault characteristic frequency of a certain stage of ring gear will modulate the meshing frequency of different gear trains in a way of amplitude modulation and frequency modulation,which shows the phenomenon of coupling modulation.In the view of this special modulation law,this article establishes the coupling modulation model of vibration signal under two-stage ring gear fault.On this basis,the parameter identification technology based on local mean decomposition and Levinberg-Marquart algorithm is proposed to determine the amplitude modulation coefficient of the fault model.Then,the condition indicator can be easily constructed to achieve the purpose of ring gear fault monitoring.The field data are tutilized to evaluate the content of this paper.Results show that the new model describes the vibration signal more comprehensively than the traditional model.The index constructed by parameter identification technology can locate the fault gear.
作者
刘少康
武英杰
田野
王建国
刘长良
Liu Shaokang;Wu Yingjie;Tian Ye;Wang Jianguo;Liu Changliang(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China;School of Automation Engineering,Northeast Electric Power University,Jinlin 132012,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第10期260-269,共10页
Chinese Journal of Scientific Instrument
基金
中央高校基本科研业务费(2020MS117)项目资助。
关键词
风电齿轮箱
振动信号模型
参数辨识
状态监测
wind turbine gearbox
vibration signal model
parameter identification
condition monitoring