基于WT特征增强的cICA带式输送机齿轮箱故障诊断

约束独立分量分析对于测量信号中的传感器噪声(测量噪声)具有很强的免疫能力,但对源噪声的免疫性却很差。针对这个问题,提出了小波变换特征增强的约束独立分量分析的齿轮箱故障特征提取方法。通过对测量信号小波分解,有针对性地选择某子频段小波系数重构,有利于提高信噪比,增强信号的统计独立性和非高斯性,从而增强约束独立分量分析方法提取齿轮故障特征的效果;而未经小波变换除噪时,约束独立分量分析的效果不佳。通过仿真分析和在矿用带式输送机齿轮箱故障诊断的应用结果综合表明,该方法能有效降低源噪声的影响,准确提取出齿轮故障特征,尤其是微弱低频故障特征。为矿用齿轮箱多通道振动状态监测与故障诊断提供了一种新的有效手段和途径。

Fault diagnosis of belt conveyor gearbox based on WT feature-enhanced cICA

Constrained independent component analysis (cICA) algorithm has a strong denosing ability for measured noise mixed in measured signals,but is very poor for source signal with source noise.To overcome this problem,a method of gearbox fault feature extraction based on wavelet transform(WT) feature-enhanced cICA is proposed.It can improve signal-to-noise ratio (SNR) and enhance the analysis effect of cICA algorithm with the wavelet decomposition of measured signal and reconstruction for a certain sub-band wavelet coefficients.However,the analysis effect of cICA method is not good without WT denosing.The results of simulation analysis and fault diagnosis of mine belt conveyor gearbox show that the proposed method can reduce the influence of source noise,and extract the gear fault feature,especially weak low-frequency fault feature.It provides a new effective approach and mean for the multi-channel vibration condition monitoring and fault diagnosis of mine gearbox.