行星齿轮箱振动中，齿轮相关振动分量通常具有相对较大的能量，同时轴承滑移会造成行星轮轴承故障对应振动分量的特征频率获取困难。为此，提出一种基于参数优化最大二阶循环平稳盲解卷积（cyclostationary blind deconvolution ,CYCBD）的行星轮轴承故障提取方法。该方法针对CYCBD技术在轴承滑移条件下难以获取循环频率和滤波器长度的问题，以改进的包络谱故障特征比（improved fault feature ratio，IFFR）指标作为粒子群算法的适应度函数，自动获取CYCBD算法中实际的循环频率和优化滤波器长度，利用参数自适应的CYCBD算法增强了轴承故障冲击。通过解卷积结果的平方包络谱反映轴承故障特征，达到准确提取故障特征的目的。故障仿真和实验研究结果表明，本文方法可以有效提取行星轮轴承故障特征。
Abstract
In the vibration signal of planetary gearbox, the gear related vibration component usually has relatively high energy. At the same time, bearing slip will make it difficult to obtain the characteristic frequency of the vibration component corresponding to planet bearing fault. Therefore, a fault extraction method of planet bearing based on parameter optimized Maximum Second Order Cyclostationary Blind Deconvolution (CYCBD) is proposed. Aiming at the problem that it is difficult for CYCBD to obtain the cycle frequency and filter length under the condition of bearing slip, the improved envelope spectrum fault feature ratio (IFFR) index is used as the fitness function of particle swarm optimization algorithm to automatically obtain the actual cycle frequency and optimize the filter length in CYCBD algorithm, and the parameter adaptive CYCBD algorithm is used to enhance the impact of bearing fault. The square envelope spectrum of deconvolution results reflects the bearing fault characteristics, so as to extract the fault characteristics accurately. The simulation and experimental results show that this method can effectively extract fault features of planet bearing.
关键词
最大二阶循环平稳盲解卷积 /
行星齿轮箱 /
轴承故障诊断 /
故障特征比
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Key words
Maximum second order cyclostationary blind deconvolution /
Planetary gearbox /
Bearing fault diagnosis /
Fault characteristic ratio
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