基于EEMD和改进VPMCD的滚动轴承故障诊断方法

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.

A Fault Diagnosis Method for Rolling Bearing Based on EEMD and Improved VPMCD

Aiming at the defects of parameter estimation in VPMCD, BP neural network nonlinear regression method was used instead of the least squares method to solve the ill-conditioned problem that exists 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 signal 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.