摘要
在声发射信号的信息熵距的基础上,提出了声发射信号的信息熵均距诊断方法,该方法能有效提升汽轮机上滑动轴承润滑状态的诊断精度,通过突出润滑状态的信息熵特征和改变信息熵点之间的距离算法,使不同润滑状态之间的差异更明显,以增强对润滑状态的准确识别能力。该效果在半干摩擦状态的诊断上表现最显著,并在实际机组上验证了该方法的有效性,为滑动轴承润滑状态的诊断和故障预测提供了更可靠的方法。
On the basis of the information entropy distance of acoustic emission signals,an information entropy average distance diagnosis method for acoustic emission signals is proposed.This method can effectively improve the diagnostic accuracy of lubrication status of sliding bearings on steam turbines.By highlighting the information entropy characteristics of lubrication status and changing the distance algorithm between information entropy points,the differences between different lubrication statuses are more obvious to enhance the accurate recognition ability of lubrication status.This effect is most significant in the diagnosis of semi-dry friction state,and its effectiveness has been verified on actual units,providing a more reliable method for the diagnosis and fault prediction of sliding bearing lubrication state.
作者
谭浩宇
颜毅斌
张骁
陈清化
TAN Haoyu;YAN Yibin;ZHANG Xiao;CHEN Qinghua(Hunan High Speed Railway Operation Safety Assurance Engineering Technology Research Center,Zhuzhou,Hunan 412000,China)
出处
《自动化应用》
2024年第4期139-142,145,共5页
Automation Application
基金
湖南省教育厅科学研究项目(19C1216,20C1226)。
关键词
滑动轴承
润滑状态
声发射
信息熵
信息熵距
sliding bearing
lubrication state
AE signals
information entropy
information entropy distance
