HHT在齿轮故障诊断问题中的应用

齿轮作为一种重要的机械传动设备,对其进行故障诊断具有重要意义。传统的齿轮故障诊断大都采用FFT或者小波分析方法,对处理具有非平稳性的齿轮故障信号效果不太明显。HHT由经验模态分解方法(EMD)和Hilbert变换两步组成。经验模态分解方法(EMD)把时间序列信号分解成不同特征时间尺度的固有模态函数(IMF),适合处理非线性和非平稳过程。Hilbert变换对具有调制现象的信号的分析有很大优势。介绍了该方法的基本原理,并将HHT应用于齿轮断齿故障诊断,齿轮故障实验信号的研究结果表明:基于HHT的分析方法,能有效地诊断齿轮的故障。

Application of Hilbert-Huang Transform to Gear Fault Diagnosis

Gear as an important mechanical transmission equipment,its fault diagnosis has important significance. Traditional gear fault diagnosis most use FFT or wavelet analysis method,and it has no noticeable effect in handling gear fault non-stationary signal. Hilbert-Huang transform is composed of empirical mode decomposition and Hilbert transform. Empirical mode decomposition separate the analyzed signals into components with different time scale,and is fit to do with nonlinear and nonstationary signal. Hilbert transform has advantage in analysising the signal with modulating.The experimental results show that this method based on Hilbert-Huang transform can effectively diagnose the faults of geer.