基于非平稳时间序列双标度指数特征的齿轮箱故障诊断

齿轮箱振动信号是含噪的多分量非平稳信号,故障模式相近的复杂齿轮箱振动信号的分类是齿轮箱故障诊断的难点。采用去趋势波动分析(Detrended fluctuation analysis,DFA)方法分析齿轮箱振动信号的标度行为,随着时间尺度的增大,标度指数会突然发生改变,形成双标度指数特征,由双标度指数构成的二维矢量具有明确的物理意义,可以作为表征时间序列内部动力学机制的特征参数。提出一种基于非平稳时间序列双标度指数特征的齿轮箱故障诊断方法,对正常状态和含有轻度磨损、中度磨损和断齿故障的齿轮箱故障诊断的结果表明,提出的方法成功地解决了故障模式相近的复杂齿轮箱振动信号的分类问题,很好地克服了傅里叶变换、小波变换和基于单标度指数的方法存在的缺陷,为齿轮箱的故障诊断提供了一种新的方法。

Fault Diagnosis of Gearboxes Based on the Double-scaling-exponent Characteristic of Nonstationary Time Series

Gearbox fault data are usually noisy,multicomponent,and nonstationary.As a result,the classification of complex gearbox vibration data with similar fault patterns is still one of intractable problems for gearbox fault diagnosis.Detrended fluctuation analysis(DFA) is utilized to analyze the scaling behavior of gearbox vibration data and the scaling exponent will have an abrupt change with the gradual increase in time scales.Thus,a two-dimensional vector containing two scaling exponent carries definite physical meaning and can be used as feature parameters to describe the underlying dynamic mechanism hiding in gearbox vibration data.Consequently,a novel method for gearbox fault diagnosis based on the double-scaling-exponent characteristic of nonstationary time series is proposed.Moreover,the proposed method,as well as Fourier transform,wavelet transform and the single-scaling-exponent method,is exploited to classify the normal,slight-worn,medium-worn and broken-gear vibration data from a four-speed motorcycle gearbox.The results show that the proposed method is sensitive to the subtle differences between two similar fault patterns and capable of solving the classification of two complex gearbox vibration data with similar fault patterns,which another three methods fail to do.