摘要
针对齿轮箱振动故障特征难提取的问题,提出一种改进的经验小波变换(EWT)与希尔伯特黄(HHT)边际谱结合的故障特征提取方法。通过EWT与改进EWT方法作对比分析,采用改进EWT对信号进行分解不仅得到的信号分量信噪比高,分量数目合理,而且可以实现自适应对信号的频谱趋势进行划分,具有更好的信号分离能力。通过振动故障模拟实验对故障齿轮的振动信号进行变换与边际谱分析,结果表明该方法可以有效提取齿轮箱振动故障特征。
Aiming at the problem that gearbox vibration fault features are difficult to extract,an improved fault feature extraction method combining empirical wavelet transform(EWT)and Hilbert-Huang(HHT)marginal spectrum is proposed.Through comparative analysis of EWT and improved EWT method,the use of improved EWT to decompose the signal not only has a high signal-to-noise ratio and reasonable number of components,but also can realize adaptive division of the signal′s spectrum trend,which has better signal separation ability.Through the vibration fault simulation experiment,the vibration signal of the faulty gear is transformed and the marginal spectrum analysis is carried out.The results show that the method can effectively extract the vibration fault characteristics of the gearbox.
作者
张思思
刘玉波
ZHANG Si-si;LIU Yu-bo(School of Mechanical Engineering,Heilongjiang University of Science and Technology,Harbin 150022,China)
出处
《煤炭技术》
CAS
北大核心
2021年第11期220-223,共4页
Coal Technology
