基于变分模式分解的滑动轴承摩擦故障特征提取与状态识别  

Feature Extraction and State Recognition for Sliding Bearing Friction Faults Based on Variational Mode Decomposition

作　　者：张云强[1] 张培林[1] 王怀光[1] 杨玉栋[2] ZHANG Yunqiang ZHANG Peilin WANG Huaiguang YANG Yudong(Department of Vehicle and Electrical Engineering,Ordnance Engineering College,Shijiazhuang 050003, China The 4th Department, Wuhan Mechanical Technology College, Wuhan 430075, China)

机构地区：[1]军械工程学院车辆与电气工程系,石家庄050003 [2]武汉军械士官学校四系,武汉430075

出　　处：《内燃机工程》2017年第4期89-96,共8页Chinese Internal Combustion Engine Engineering

基　　金：国家自然科学基金项目(E51205405;51305454)~~

摘　　要：针对滑动轴承振动信号明显的非线性非平稳性及信号中摩擦信号微弱等特点,提出一种基于变分模式分解(VMD)的滑动轴承摩擦故障特征提取与状态识别方法。采用VMD对滑动轴承振动信号进行分解,将其自适应地分解为系统冲击信号、低频摩擦信号和高频摩擦信号3个分量,在此基础上定义并提取相对频谱能量矩特征参数,用于描述滑动轴承振动信号及其各分量的特征。对S195-2型柴油机曲轴轴承摩擦故障信号进行了分析,K-近邻分类器的平均识别精度达到93.3%。研究结果表明:基于VMD分解的相对频谱能量矩特征对滑动轴承的工作状态比较敏感,能有效识别其摩擦故障状态。In view of the sliding bearing nonlinear and non-stationary vibration signals and its weak friction signals, a method of feature extraction and state recognition method for sliding bearing friction faults based on variational mode decomposition（VMD） was proposed. The VMD was used to adaptively decompose the sliding bearing vibration signals into the system shock signal, low frequency friction signal and high frequency friction signal. On the basis of VMD decomposition, the feature parameters of relative frequency spectrum energy moments were defined and extracted, which were used to describe the characteristics of sliding bearing vibration signals and their components. The crankshaft bearing friction fault signals from a S195-2 diesel engine were analyzed, and the average recognition accuracy of K-nearest neighbor classifier reached 93.3%. Results indicate that the relative frequency spectrum energy moments extracted on the basis of VMD are sensitive the working conditions of sliding bearing and can effectively identify its friction fault states.

关 键 词：内燃机 柴油机 滑动轴承 变分模式分解 特征提取 状态识别 

分 类 号：TK411[动力工程及工程热物理—动力机械及工程]