摘要
为全面提取转子故障时振动信号特征,提高故障诊断的可靠性,提出了一种基于全矢包络谱的旋转机械故障诊断方法。首先,利用正交采样技术获取转子同一截面上互相垂直的振动信号,将其组成复数信号;然后,运用基于能量阀值的二元经验模态分解(BEMD)将复数信号分解成系列复固有模态函数分量(CIMFs),利用希尔伯特变换(HT)解调CIMFs获得复包络信号;最后,通过全矢谱技术融合复包络信号得到全矢包络谱,在此基础上,进行故障诊断。柔性转子和高炉煤气余压透平发电装置故障诊断结果证明了所提方法的有效性和可行性。
In order to fully extract the characteristics of vibration signal and improve the reliability of fault diagnosis,a mechanical fault diagnosis method based on FVES(Full Vector Envelope Spectrum) is proposed.Firstly,the orthogonal sampling technique is used to obtain the mutually perpendicular rotor vibration signal in the same section and composited them to a complex signal. Secondly,this complex signal is divided into series of CIMFs(Complex Intrinsic Mode Functions) based on BEMD(Bivariate Empirical Mode Decomposition),which are demodulated by Hilbert transform to get the envelope signal of CIMFs. Finally,the complex envelope signal is fused by Full Vector Spectrum technology to get corresponding FVES for fault diagnose.The fault diagnosis results of rubbing rotor and the blast furnace top gas recovery turbine unit show that the proposed method is accurate and complete.
出处
《电力自动化设备》
EI
CSCD
北大核心
2018年第1期184-192,共9页
Electric Power Automation Equipment
基金
国家自然科学基金重点资助项目(61134002)
中央高校基本科研业务费专项资金资助项目(2682015CX025)
河南省创新型科技人才队伍建设工程项目(C20150034)
2018年度河南省高等学校重点科研项目(18A460006)
郑州工程技术学院科技创新团队建设计划资助项目(CXTD2017K1)~~
关键词
全矢包络谱
二元经验模态分解
复固有模态函数
希尔伯特变换
复包络信号
信息融合
full vector envelope spectrum
bivariate empirical mode decomposition
complex intrinsic mode functions
Hilbert transform
complex envelope signal
information fusion