摘要
为了从齿轮箱振动信号中快速获得各零部件的振动信号,给准确定位故障部件提供理论依据,提出了一种适合于齿轮箱振动信号周期分量提取的改进型粒子群优化算法。在对齿轮箱振动信号、周期信号振幅与对应频率间的关系以及初始相位与对应频率间的关系分析的基础上,以各特征频率及其组合为基准生成初始种群进行优化,减少了周期信号提取中的盲目性,加快了周期信号的提取过程。最后,对实验室的齿轮箱进行了不同工况下振动信号的检测,以这些信号为例进行了周期分量的提取,结果说明所提方法是可行有效的。
In order to obtain the various components vibration signals from quickly gearbox vibrancy signals,which can provide the theory basis to the accurate localization failed part.An advanced particle swarm optimization algorithm is proposed,which is suitable for periodic component extraction from gearbox vibrancy signals.On the basic of analysis and study for gearbox vibration signal,the relationship between periodic signal amplitude and the corresponding frequency and the relationship between periodic signal initial phase and the corresponding frequency,selecting various characteristic frequency and their combinations as initial population.It can reduce the blindness in the periodic signal extraction and speed up the extraction process of the periodic signals.Finally,the different condition vibration signals of the laboratory gearbox were detected and periodic component were extracted with those signals as examples.The results show that the proposed method is feasible and effective.
出处
《火力与指挥控制》
CSCD
北大核心
2013年第1期30-33,共4页
Fire Control & Command Control
基金
国家自然科学基金资助项目(50575214)
关键词
振动信号
周期分量提取
改进型粒子群优化
vibration signal
periodic component extraction
advanced particle swarm optimization
