摘要
针对滚动轴承早期故障难以识别的问题,提出一种自适应白噪声的完备总体经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)和1.5维谱相结合的滚动轴承故障诊断方法。该方法首先运用CEEMDAN对振动信号进行分解,得到一系列IMF分量,然后根据峭度准则以及相关系数准则提取一个包含主要故障信息的IMF分量,最后对提取的IMF分量进行1.5维谱分析,通过分析谱图中突出成分以确定轴承故障类型。利用仿真信号和工程实验数据对该方法进行分析验证,所得出结果的谱图均比用单一方法得出的谱图清晰,充分证明该方法在滚动轴承早期故障诊断中的优势。
In order to solve the problem that early failure of rolling bearings information are difficult to identify, a new method of rolling bearing fault diagnosis based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and 1.5 dimension spectrum is proposed. Firstly, the CEEMDAN method is used to decompose the vibration signal, a signal of a finite number of intrinsic mode component(IMF) is obtained. Then, according to the kurtosis criterion and correlation coefficient criterion of each component, a IMF component containing important fault information is extracted. Finally, the extracted IMF component is analyzed by 1.5 dimension spectrum, fault type of bearing can be determined by analyzing the prominent components in 1.5 dimension spectrum. The method is analyzed and verified by simulation signal and engineering experiment data. The spectrum results obtained are much clearer than those obtained by single method.The advantages of this method in the early fault diagnosis of rolling bearing are fully proved.
作者
黄慧杰
孙百祎
任学平
刘淮全
HUANG Huijie;SUN Baiyi;REN Xueping;LIU Huaiquan(Institute of Mechanical Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;Taishan Campus of Shandong Transport Vocational College,Taian 271000,China)
出处
《中国测试》
CAS
北大核心
2019年第2期151-156,共6页
China Measurement & Test
基金
国家自然科学基金项目(51565046)
内蒙古自治区高等学校科学研究项目(NJZY16154)
作者简介
黄慧杰(1995-),男,山西运城市人,硕士研究生,专业方向为机械设备故障诊断及状态检测.
