计算机应用与软件2017，Vol.34Issue(9)：283-287,5.DOI:10.3969/j.issn.1000-386x.2017.09.055
基于振动信号能量熵的轴承故障诊断
BEARING FAULT DIAGNOSIS METHOD BASED ON ENERGY ENTROPY OF VIBRATION SIGNAL
摘要
Abstract
There are a lot of bearings in mechanical equipment,which is the main reason of mechanical failure.Timely and accurate judgment of the bearing fault can effectively prevent the accident caused by the bearing fault and reduce the loss.A Bearing fault diagnosis scheme based on energy entropy is proposed in this paper.Different energy entropy of the bearing vibration signal has different energy distribution in different working condition.The state of the bearing can be judged by the difference of the energy distribution.First,original acceleration vibration signals are decomposed by ensemble empirical mode decomposition (EEMD) into a finite number of stationary intrinsic mode functions (IMFs).To identify the fault pattern and condition,energy feature extracted from a number of IMFs that contained the most dominant fault information could serve as input vectors of relevance vector machine.Practical examples show that the proposed diagnosis approach can identify bearing fault patterns effectively.关键词
总体平均经验模态分解(EEMD)/能量熵/相关向量机(RVM)/故障诊断Key words
Ensemble empirical mode decomposition (EEMD)/Energy entropy/RVM/Fault diagnosis分类
信息技术与安全科学引用本文复制引用
任玉卿,王海瑞,齐磊,李荣远..基于振动信号能量熵的轴承故障诊断[J].计算机应用与软件,2017,34(9):283-287,5.基金项目
国家自然科学基金项目(61263023). （61263023）
