摘要
针对齿轮箱振动信号的非平稳特性和在现实条件下难以获得大量故障样本的实际情况,提出一种经验模态分解和支持向量机相结合的故障诊断方法。运用经验模态分解方法对齿轮箱故障的振动信号进行分析,进行EEMD分解得到相对平稳的本征模态IMF,并计算每个IMF的能量熵,将其作为支持向量机的输入特征向量以判断齿轮箱的工作状态和故障类型。
For non-stationary characteristics of vibration signals of gearbox and difficult to obtain a large number of fault samples of actual situation in real conditions, the fault diagnosis method of a combination of an empirical mode decomposition and support vector machine is put forward. Using empirical mode decomposition method to analyze vibration signals of gearbox failure to conduct EEMD decomposition is relatively stable and intrinsic mode IMF, and calculate entropy of energy of each IMF, as input feature vectors for support vector machine to determine working status of gear box and type of fault.
出处
《煤矿机械》
北大核心
2013年第2期246-247,共2页
Coal Mine Machinery
