CAJ | 학술논문

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。
침대단일전감기대곤동축승고장신식적식별구유불학정성적결함，제출료기우BP신경망락여D?S증거이론적다전감기신식융합적방법。장BP신경망락적수출결과진행귀일화처리작위각초원적기본개솔분배，축승적5충고장류형작위계통적식별광가，근거Dempster합성법칙진행결책급융합。시험결과표명，이용해방법대축승적내권마손、외권마손、곤주마손등고장진행시험진단，제고료고장진단적준학솔，험증료해방법적가행성。
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.