CAJ | 학술논문

提出基于GM(1,1)-SVM的滚动轴承故障诊断及预测方法. 首先,提取滚动轴承各类故障和正常状态下振动信号的时域及频域特征值,然后,选取重要特征参数建立预测模型,进行特征值预测;最后,使用轴承各类故障特征值和正常状态特征值训练二叉树支持向量机,构造滚动轴承决策树,判别故障,实现对故障类型的分类,从而达到对轴承故障诊断,并通过预测值与支持向量机实现故障预测的目的,突破传统算法不能有效预测轴承故障的局限性.
제출기우GM(1,1)-SVM적곤동축승고장진단급예측방법. 수선,제취곤동축승각류고장화정상상태하진동신호적시역급빈역특정치,연후,선취중요특정삼수건립예측모형,진행특정치예측;최후,사용축승각류고장특정치화정상상태특정치훈련이차수지지향량궤,구조곤동축승결책수,판별고장,실현대고장류형적분류,종이체도대축승고장진단,병통과예측치여지지향량궤실현고장예측적목적,돌파전통산법불능유효예측축승고장적국한성.
The paper put forward a method based on GM ( 1 ,1 )-SVM for rolling bearing fault prediction and diagnosis. Firstly, time and frequency domain feature values of vibration signal of rolling bearing under all kinds of fault and normal condition were extracted. Then the important characteristic parameters were collected to build the predict model. Lastly, fault and normal condition eigenvalue was used to train binary tree support vector machine and to construct the decision tree to classify the fault type. Thus the bearing fault diagnosis and the fault prediction through the predicted values and the support vector machine ( SVM) were achieved.