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针对大型齿轮箱低速轴故障信息难以提取的问题,采用小波分析方法对故障数据进行处理以实现信号在时/频域的局域性分析,将其无冗余、无泄漏地分解到一组具有紧支撑性的小波基上.文中采用小波分层突变系数作为判别故障隐患的特征值,并对该特征值进行趋势分析.结果表明:小波变换能有效捕捉冲击信号的时域特征和故障发生的时间历程,用小波分层突变系数所做的趋势图能有效地预测故障发展趋势,避免突发故障.
Aiming at the problem that the fault information of low speed shaft in large gearbox is difficult to extract, wavelet analysis is used to process the fault data to realize the local analysis of signal in time / frequency domain. The paper uses the wavelet stratification mutation coefficient as the eigenvalue to distinguish the hidden trouble and analyzes the trend of the eigenvalues.The results show that the wavelet transform can effectively capture the time-domain features of the impact signal and the occurrence of the fault Time histories, the trend graph made by using wavelet stratification mutation coefficient can effectively forecast the trend of fault development and avoid sudden failure.