摘要
针对原VPMCD方法在参数估计过程中存在的缺陷,用BP神经网络非线性回归方法代替原方法中的最小二乘法,解决了最小二乘法中存在的病态问题,因此,提出了改进多变量预测模型(Variable predictive mode based class discriminate,简称VPMCD)的滚动轴承故障诊断方法.首先采用总体经验模态分解(Ensemble empirical mode decomposition,简称EEMD)方法对滚动轴承振动信号进行分解得到若干个单分量信号,然后提取各分量奇异值组成特征向量作为改进VPMCD的输入,最后对滚动轴承工作状态和故障类型进行识别.实验结果表明,该方法能够有效地应用于滚动轴承故障诊断.
Aiming at the defects of parameter estimation in VPMCD,BP neural network nonlinear re-gression method was used instead of the least squares method to solve the ill-conditioned problem that ex-ists in the least square method.Therefore,a fault diagnosis method for rolling bearing based on improved Variable Predictive Mode on the basis of Class Discriminate (VPMCD)was proposed.Firstly,Ensemble Empirical Mode Decomposition (EEMD)approach was used to decompose the rolling bearing vibration sig-nal into a number of single components;and then,the singular values were abstracted from the component matrix and formed feature vector which will act as an input in the improved VPMCD;finally,the work states and faults pattern of the rolling bearing can be identified.The analysis results from the experimental rolling bearing vibration signals have demonstrated that the proposed method can be effectively applied to the rolling bearing fault diagnosis.
出处
《湖南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2014年第10期22-26,共5页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(51175158
51075131)
湖南省自然科学基金资助项目(11JJ2026)
关键词
改进VPMCD
EEMD方法
奇异值分解
滚动轴承
故障诊断
improved variable predictive mode based on class discriminate
ensemble empirical modedecomposition
singular value decomposition
rolling bearing
fault diagnosis