摘要
针对旋转机械不同类型故障会使振动信号具有不同形态特征及振动信号信噪比低等特点,提出基于局部均值分解(Local Mean Decomposition,LMD)与形态谱的旋转机械故障诊断方法。其中的LMD能对旋转机械原始振动信号进行降噪处理,而形态谱则能反映振动信号的形态特征,从而能判断旋转机械的工作状态。将该方法用于转子系统故障诊断,分析结果表明,该方法能有效提取旋转机械故障振动信号的故障特征,能准确识别旋转机械的故障状态。
In view of that the vibration signals of rotating machinery with different types of faults will have different morphological features and in consideration of the commonly encountered low signal to noise ratio of vibration signals, a rotating machinery fault diagnosis method based on local mean decomposition (LMD) and pattern spectrum was proposed. In the method, the original rotating machinery vibration signals were denoised by LMD, the morphological features of vibration signals were reflected by pattern spectrum, and thus the working states of rotating machinery were judged. The results of analysis of fault vibration signals of a rotor system indicate that the method can extract the fault characteristics from rotating machinery vibration signals effectively and recognize its fault states accurately.
出处
《振动与冲击》
EI
CSCD
北大核心
2013年第9期135-140,共6页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(51075131)
教育部长江学者与创新团队发展计划(531105050037)
湖南省自然科学基金资助项目(11JJ2026)
