基于小波分析的大型齿轮箱低速轴故障诊断

Fault diagnosis of low-speed shafts in large gearboxes based on wavelet analysis

  • 摘要: 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.

     

    Abstract: Aimed at the difficulty to extract the fault information of a low speed shaft in a large gearbox, wavelet analysis was used to realize the local analysis of signals in a time and frequency domain simultaneously. The signals were dissembled to a series of compactly supported wavelet bases non-redundantly and without leaking. The saltation coefficient of wavelet analysis was regarded as a characteristic value to predict a sudden accident and the changing trend of the coefficient was figured out. The results showed that wavelet transform could capture the characteristics in a time domain and the evolvement procedure of a fault. The trend graph of the coefficient could effectively predict the development trend of a fault and avoid a sudden accident.

     

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