摘要
风电机组一般采用滚动轴承支撑结构,滚动轴承不同故障模式对应的振动冲击间隔频率存在差异。为了准确地从振动信号中提取滚动轴承故障征兆,在分析风电机组滚动轴承故障机理、信号特征的基础上,提出了基于小波变换的风电机组滚动轴承故障KPI计算方法,首先对风电机组的振动信号进行小波变换及阈值去噪,并计算振动信号的小波能量谱分布图,然后以小波能量谱分布图的统计参数作为滚动轴承故障诊断的KPI,采用椭圆型判决函数法实现滚动轴承的故障诊断,现场实测信号的诊断结果验证了该方法的有效性。
The rolling bearing bracing structure has been widely used in the wind turbine units.When the rolling bearing is in failure,the spectral composition of the vibration signal is complex and varied under different fault mode.Based on the analysis of the fault mechanism and the vibration signal characteristics of rolling bearing,the fault key performance index（KPI）calculation method for rolling bearing based on wavelet transform was put forward in this paper.Firstly,the wavelet transform and threshold denoising were separately carried out on the vibration signal.And then,twodimensional map of the energy and the frequency section of wavelet transform coefficients were calculated.Finally,the statistical parameters of two-dimensional map were extracted as the fault KPI of the rolling bearing of wind turbine,and the fault diagnosis of rolling bearing was realized by using the elliptic-type decision function method.The validity of the method proposed was verified by the diagnosis results of the field test signals.
出处
《水电能源科学》
北大核心
2017年第2期189-192,共4页
Water Resources and Power
基金
郑州市科技攻关计划项目(X2013G0432)
华北水利水电大学高层次人才科研启动项目(201316)
关键词
风电机组
滚动轴承
频谱
振动
小波变换
故障诊断
wind turbine unit
rolling bear
frequency spectrum
vibration
wavelet transform
fault diagnosis
