摘要
利用小波包分解方法对在线监控中收集到的齿轮箱振动信号进行频域划分,并在其划分后的频段上有选择地进行了信号重构。在其基础上提出齿轮在线健康监控指数,可以用来反映齿轮箱整个寿命期间的健康状态。同时,为了实现对齿轮早期故障的预警,提出了一个用来在线检测的动态阈值。通过3套不同的齿轮箱全寿命振动信号数据进行了健康指数和早期预警的验证。实验结果证明,该健康监控指数及其动态阈值可以准确地检测出齿轮早期故障发生的时刻。
A wavelet packet method is utilized to decompose vibration signal collected from gearbox online monitoring systems. Signal reconstruction is implemented based on optimal selection of selected frequency domain A health monitoring index is proposed, which can reflect health condition of gearbox during whole life cycle. In order to realize health warning, a dynamic threshold is proposed to detect the existence of early gear failure. Three sets of whole lifetime vibration data collected from gearboxes are used to validate this method. The analysis results illustrate that the proposed method has good performance in detecting early gear failure and owns great potential in engineering applications.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2010年第1期157-160,共4页
Journal of University of Electronic Science and Technology of China
基金
高等学校博士学科点专项科研基金新教师基金(20070614023)
中国博士后科学基金(20070420223)
关键词
动态阈值
早期故障
在线健康监控指数
旋转设备
小波包
dynamic threshold
incipient failure
online health monitoring index
rotating machinery
wavelet packet
