摘要
针对在强背景噪声情况下,齿轮故障信号信噪分离难,给故障诊断带来麻烦的问题,提出了一种基于形态小波去噪的齿轮故障诊断方法。方法结合了数学形态学的特征识别和小波分解的多分辨率分析特性,先采用形态小波方法对齿轮的振动信号进行消噪预处理,再计算信号的时频谱和功率谱,提取故障特征。给出了形态小波方法在齿轮故障诊断中的应用原理、方法步骤和评价指标。仿真和实验结果表明,方法可以有效地去除强噪声的干扰,提高信噪比,突现出信号的故障特征,提高了齿轮故障诊断的精度。
The gear fault signal is often submerged by a lot of background noise, so it is hard for signal-noise separation and for further fault diagnosis. A novel diagnosis method is proposed to performance the pretreatment of gear fault signal based on morphological wavelet de-noising. This method combines the characteristics of identification from mathematical morphology and the characteristics of multi-resolution analysis from wavelet decomposition. Firstly the gear vibration signal is de-noised by using the morphological wavelet de-noising method. Then the fault features is extracted by calculating the power spectrum of de-noised signal. The principle and steps of method are given and its de-noising effect is evaluated by using some parameters. The simulated signal and actual signal is analyzed. The results show that the proposed method can suppress the noise interference greatly. After de-noising, the SNR is improved and the fault characteristic of signal is highlighted better. The accuracy of fault diagnosis of bearing is improved effectively. And the proposed method has good engineer practicability.
出处
《机械强度》
CAS
CSCD
北大核心
2015年第3期398-402,共5页
Journal of Mechanical Strength
基金
国家自然科学基金项目(41304098)
湖南省自然科学基金项目(12JJ4034)
湖南省教育厅青年项目(13B076)
湖南省重点建设学科-光学基金
湖南省重点实验室"光电信息集成与光学制造技术"
"湖南省光电信息技术校企联合人才培养基地"资助~~
关键词
形态小波
去噪
齿轮
故障诊断
Morphological wavelet
De-noising
Gear
Fault diagnosis
