【摘要】 多重分形去趋势波动分析(Multifractal Detrended Fluctuation Analysis,MFDFA)算法只能处理一维时间序列,当采用MFDFA算法分析复杂的齿轮箱故障信号时,其分析结果容易受到信号噪声及其它因素的干扰。解决这个问题的有效方法是采用多维数据分析方法,为此本文采用多重分形去趋势互相关分析(Multifractal Detrended Cross-correlation Analysis,MF-DCCA)算法来分析齿轮箱振动信号,提出了基于MF-DCCA算法的齿轮箱故障诊断方法。该方法首先计算二维振动信号的互相关函数,然后再分析互相关函数的多重分形特征。将本文所提出的方法用于分析实际齿轮箱的振动信号,结果表明该方法能够区分相近的齿轮箱故障模式,在齿轮箱故障诊断中取得了良好的效果,与基于MFDFA算法的方法相比具有明显的优势。更多还原

【Abstract】 The multifractal detrended fluctuation analysis( MFDFA) is developed only for processing one- dimension time series. Consequently,when applied to analyze complex vibration data from a defective gearbox,the MFDFA frequently produces poor results due to disturbance from noise and other factors. As a result,high- dimension data analysis seems effective in solving this problem. To this end,multifractal detrended cross- correlation analysis( MF- DCCA) is introduced to examine gearbox vibration data and then a novel method for fault diagnosis of gearboxes is proposed based on MF- DCCA. In the proposed method,a cross- correlation function of two- dimension signals is firstly estimated and then the multifractal feature hidden in the cross- correlation function are analyzed. Application to probing realist gearbox vibration data suggested that the proposed method can clearly distinguish between similar and close fault patterns of gearboxes and performs better than the method based on MFDFA in fault diagnosis of gearbox.更多还原