神经网络和改进D-S证据理论相结合的滚动轴承复合故障诊断研究  

Composite Fault Diagnosis Research of Rolling Bearing Based on Combination of Neural Network and Improved D-S Evidence Theory

作　　者：李善[1] 谭继文[1] 俞昆 

机构地区：[1]青岛理工大学机械工程学院,山东青岛266520

出　　处：《机床与液压》2018年第1期153-157,184,共6页Machine Tool & Hydraulics

基　　金：国家自然科学基金资助项目(51075220);山东省高等学校科技计划项目(J13LB11);高等学校博士学科点专项科研基金(20123721110001);青岛市科技计划基础研究项目(12-1-4-4-(3)-JCH)

摘　　要：提出了将神经网络与D-S证据理论相结合的故障诊断方法,实现了故障信号的特征级和决策级融合,并应用于轴承的复合故障诊断研究。将BP、RBF、GRNN 3种神经网络的输出结果作为3个证据体,滚动轴承的4种复合故障特征作为系统的识别框架,引入聚类系数作为权值分配,重新计算基本概率赋值,对D-S证据理论进行改进,以提高轴承复合故障诊断的准确性。A fault diagnosis method based on the combination of neural network and Dempster/Shafer （D-S） evidence theory is proposed. Feature level and decision level fusion of fault signal was realized, which was applied in research of the composite fault diagnosis of bearing. The output results of back propagation （ BP ）, radial-based function （ RBF）, general regression neural network （GRNN） three kinds of neural networks were used as three body of evidence. Four kinds of compound fault characteristics of rolling bearing were regarded as system identification framework. Clustering coefficient was introduced as the weight distribution, and the basic probability assignment was recalculated. The D-S evidence theory is improved to improve the accuracy of the composite fault diagnosis of bearing.

关 键 词：滚动轴承 复合故障诊断 神经网络 聚类系数 D-S证据理论 

分 类 号：TH17[机械工程—机械制造及自动化]