摘要
针对包络解调和形态闭算子易受强噪声和低频谐波分量干扰的缺点,提出了采用形态梯度解调算子提取脉冲信号的方法。对受到低频干扰的仿真脉冲调制信号和实测齿轮断齿故障信号的分析结果表明,形态梯度解调算子既抑制了噪声又充分突出了故障信号的冲击特征,具有更强的噪声抑制和脉冲提取能力,完全不受低频分量的干扰,且计算简单、快速,为齿轮故障特征提取提供了一种有效的方法。
A method based on the morphological gradient(MG)filter was proposed for feature extraction of gear fault signal.A comparison was made between the conventional envelope demodulation and the morphological close method,and the latter method was applied to the simulated signals and the measured gear fault signals interfered by low-frequency harmonic signals and random noise.The results demonstrate that the MG filter can effectively suppress the noise and clearly highlight the impulse feature of the original fault signals and be entirely free from interferences of the low frequency harmonic.Moreover,the computation cost of the MG filter is much less.So,the MG demodulation filter provides an efficient approach to feature extraction for gear fault diagnoses.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2010年第1期39-42,共4页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(编号:50705097)
河北省自然科学基金资助项目(编号:E2007001048)
关键词
数学形态学
形态梯度
齿轮故障诊断
特征提取
mathematical morphology morphological gradient gear fault diagnosis feature extraction
