齿轮与轴承故障的Chirplet时频分析

Chirplet Time-Frequency Spectrum Analysis of Gear and Rolling Bearing Fault

机械故障信号通常具有非线性时频关系多分量信号,其频谱占有较宽的频带,且各分量的频谱往往相互交叠,给故障诊断带来了很大的障碍。首先,在分析Ch irp let时频分解的理论基础上,对Ch irp let基函数进行了增加曲率参数的改进,提高了基函数对非线性时变分量的匹配能力;然后,仿真试验对基函数改进后的自适应时频分布与其他各类时频谱进行了比较。结果表明,改进后的基函数能很好地匹配出非平稳信号中的各种线性或非线性时变分量,其对应的时频具有较高的时频分辨率,且无交叉干扰项;最后,将该方法用于轴承故障信号和齿轮故障信号的分析。分析结果证实,该方法不仅能对轴承与齿轮箱故障进行准确定位,而且为故障原因及故障程度提供可靠的判断依据。

Machine fault signal generally includes multiple non-stationary components with nonlinear time-frequency(TF) relationship,whose wideband frequency spectrums are usually overlapped.This brings great obstacles for machine fault diagnosis.Firstly,the principle of Chirplet TF decomposition is analyzed,and an additional curvature parameter is introduced into the traditional Chirplet elementary function to match the time varying linear or non-linear components.The performance comparison between the modified version of adaptive TF representation and the other TF representation verifies that the modified version has the high TF resolution without cross-term interference and can match the non-stationary component very well.Finally,the practical fault diagnosis examples of roll bearing and gear box are given.The satisfactory results for diagnosis are acquired with the modified adaptive TF representation.