基于多传感器信息融合的贝叶斯网络故障诊断方法研究及应用
|
摘要
针对齿轮泵信号具有复杂性和模糊性的特点,提出了一种基于多传感器信息融合的贝叶斯网络故障诊断方法。分析了齿轮泵振动和压力信号特点,以此为基础提取了振动信号的能量特征、分形特征和压力信号的高频压力脉动3种特征属性,构建了多故障贝叶斯网络对特征进行融合,设计了贝叶斯分类器,通过最大后验概率准则识别故障类型。两次融合结果表明:多传感器信息完备了特征空间,提高了诊断正确率,能够有效实现齿轮泵多种故障的诊断,具有较好的应用价值。
Aiming to the complex and fuzz nature of gear pump signal,a fault diagnosis approach of Bayesian networks was presented based on multi-sensor information fusion.Firstly,this study analyzed the signal characteristic of vibration and pressure,and extracted energy and fractal characteristis of vibration signal and high frequency pressure pulse of pressure signal.Then the multi-fault Bayesian network was built to fuse these characteristics.Finally,Bayesian classifier was designed to identify the fault pattern through the maximum posterior estimation.Twice fusion results show that multi-sensor information completes the characteristic space,raises the diagnosis exactitude rate.This approach can effectively carry out the gear pump multi-fault diagnosis and have better application value.
Aiming to the complex and fuzz nature of gear pump signal,a fault diagnosis approach of Bayesian networks was presented based on multi-sensor information fusion.Firstly,this study analyzed the signal characteristic of vibration and pressure,and extracted energy and fractal characteristis of vibration signal and high frequency pressure pulse of pressure signal.Then the multi-fault Bayesian network was built to fuse these characteristics.Finally,Bayesian classifier was designed to identify the fault pattern through the maximum posterior estimation.Twice fusion results show that multi-sensor information completes the characteristic space,raises the diagnosis exactitude rate.This approach can effectively carry out the gear pump multi-fault diagnosis and have better application value.
引文
[1]李弼程,黄洁,高世海等.信息融合技术及其应用[M].北京:国防工业出版社,2010
[2]李俭川,胡茑庆,秦国军等.贝叶斯网络理论及其在设备故障诊断中的应用[J].中国机械工程,2003,14(10):896～900
[3]朱伟明.基于贝叶斯融合的柴油机性能状态评估方法研究[J].内燃机,2010,(2):18～19
[4]姜万录,刘思远.多特征信息融合的贝叶斯网络故障诊断方法研究[J].中国机械工程,2010,21(8):940～945
[5]潘伟.工程机械液压系统状态监测与故障诊断研究[D].西安:第二炮兵工程学院,2005
[6]Su H S.Multi-source fuzzy information fusion method based onbayesian optimal classifier[J].Acta Automatica Sinica,2008,34(3):282～287
[7]王江萍,孙文莉.基于小波包能量谱齿轮振动信号的分析与故障诊断[J].机械传动,2011,35(1):55～58
[8]钟明寿,龙源,谢全民等.基于分形盒维数和多重分形的爆破地震波信号分析[J].振动与冲击,2010,29(1):7～11
[9]Bezdek J C.Pattern Recognition with Fuzzy Objective Func-tion Algorithms[M].New York:Plenum Press,1981
[2]李俭川,胡茑庆,秦国军等.贝叶斯网络理论及其在设备故障诊断中的应用[J].中国机械工程,2003,14(10):896～900
[3]朱伟明.基于贝叶斯融合的柴油机性能状态评估方法研究[J].内燃机,2010,(2):18～19
[4]姜万录,刘思远.多特征信息融合的贝叶斯网络故障诊断方法研究[J].中国机械工程,2010,21(8):940～945
[5]潘伟.工程机械液压系统状态监测与故障诊断研究[D].西安:第二炮兵工程学院,2005
[6]Su H S.Multi-source fuzzy information fusion method based onbayesian optimal classifier[J].Acta Automatica Sinica,2008,34(3):282～287
[7]王江萍,孙文莉.基于小波包能量谱齿轮振动信号的分析与故障诊断[J].机械传动,2011,35(1):55～58
[8]钟明寿,龙源,谢全民等.基于分形盒维数和多重分形的爆破地震波信号分析[J].振动与冲击,2010,29(1):7～11
[9]Bezdek J C.Pattern Recognition with Fuzzy Objective Func-tion Algorithms[M].New York:Plenum Press,1981
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心