摘要
针对单一传感器对滚动轴承故障信息的识别具有不确定性的缺陷,提出了基于BP神经网络与D-S证据理论的多传感器信息融合的方法。将BP神经网络的输出结果进行归一化处理作为各焦元的基本概率分配,轴承的5种故障类型作为系统的识别框架,根据Dempster合成法则进行决策级融合。试验结果表明,利用该方法对轴承的内圈磨损、外圈磨损、滚珠磨损等故障进行试验诊断,提高了故障诊断的准确率,验证了该方法的可行性。
Aimed at the defect of uncertainty of single sensor for the rolling bearing fault information recognition, the method of multi-sensor information fusion was proposed based on the BP neural network and the D-S evidence theory. Output results of BP neural network were normalized as the focal element of the basic probability assignment, five kinds of fault types of rolling bearing were identi-fied as a system framework, and decision level fusion was made according to Dempster combination rule. The test results show that using the method in experiments of fault diagnosis for bearing inner ring wear, outer ring wear and ball bearing wear has improved the accuracy of fault diagnosis, and verified its feasibility.
出处
《机床与液压》
北大核心
2014年第23期188-191,共4页
Machine Tool & Hydraulics
基金
国家自然科学基金项目(51075220)
青岛市科技计划基础研究项目(12-1-4-4-(3)-JCH)
作者简介
徐卫晓（1988-）,女,硕士研究生,从事机械无损检测与故障诊断研究.E-mail：291170325＠qq.com.谭继文,E-mail：tanfanye＠sina.com.
