基于神经网络的轴承故障预测模型

被引：30

作者：

韩昕锋 ^{[1
]}

任立坤 ^{[2
]}

机构：

[1] 海军装备部装备采购中心

[2] 海军航空工程学院系

来源：

海军航空工程学院学报 | 2015年 / 03期

关键词：

故障预测; 神经网络; 状态监测;

D O I：

暂无

中图分类号：

TH165.3 []; TP183 [人工神经网络与计算];

学科分类号：

140502 [人工智能];

摘要：

利用在故障预测领域广泛应用的神经网络模型,对轴承监测数据的特征提取与建模,挖掘出监测数据与剩余寿命间内在关联,从而对轴承剩余寿命做出评估。在轴承全寿命数据的实际实验中,证实了该模型的有效性。

引用

页码：281 / 285

页数：5

共 12 条

[1]

基于贝叶斯网络的复杂系统故障预测 [J].

许丽佳 ;

王厚军 ;

龙兵 .

系统工程与电子技术, 2008, (04) :780-784

[2]

Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept[J] Susana Ferreiro;Aitor Arnaiz;Basilio Sierra;Itziar Irigoien Expert Systems With Applications 2011,

[3]

A dynamic multi-scale Markov model based methodology for remaining life prediction[J] Jihong Yan;Chaozhong Guo;Xing Wang Mechanical Systems and Signal Processing 2010,

[4]

A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology[J] Ming Dong;David He Mechanical Systems and Signal Processing 2006,

[5]

An adaptive predictor for dynamic system forecasting[J] Wilson Wang Mechanical Systems and Signal Processing 2006,

[6]

A prognostic algorithm for machine performance assessment and its application[J] Jihong Yan;Muammer Koc;Jay Lee Production Planning & Control 2004,

[7]

Gear fatigue crack prognosis using embedded model; gear dynamic model and fracture mechanics[J] C. James Li;Hyungdae Lee Mechanical Systems and Signal Processing 2004,

[8]

Predicting Remaining Life by Fusing the Physics of Failure Modeling with Diagnostics[J] G. J. Kacprzynski;A. Sarlashkar;M. J. Roemer;A. Hess;B. Hardman JOM 2004,

[9]

Prognosis of machine health condition using neuro-fuzzy systems[J] Wilson Q. Wang;M.Farid Golnaraghi;Fathy Ismail Mechanical Systems and Signal Processing 2003,

[10]

STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS[J] Y. LI;T.R. KURFESS;S.Y. LIANG Mechanical Systems and Signal Processing 2000,

← 1 2 →