一种基于Hilbert-Huang变换和AR模型的滚动轴承故障诊断方法

提出了一种基于Hilbert-Huang变换和AR模型的滚动轴承故障诊断方法.采用Hilbert-Huang变换将滚动轴承振动信号分解成若干个平稳的IMF(IntrinsicModeFunction)分量,求出每一个IMF分量的瞬时幅值和瞬时频率,然后对每一个IMF分量的瞬时幅值和瞬时频率序列建立AR模型,以模型主要的自回归参数和残差的方差作为特征向量建立Mahalanobis距离判别函数,进一步判断滚动轴承的工作状态和故障类型.实验结果分析表明,本文方法能有效地应用于滚动轴承的故障诊断.

A Fault Diagnosis Approach for Roller Bearings Based on Hilbert-Huang Transform and AR Model

A fault diagnosis approach for roller bearings based on Hilbert-Huang transform and AR model is proposed. The Hilbert-Huang transform is used to decompose the vibration signal of a roller bearing into a number of IMF components and the instantaneous amplitudes and frequencies of each IMF component are obtained. Then the AR model of each instantaneous amplitude and frequency sequence is established. The main auto-regressive parameters and the variances of remnant are regarded as the feature vectors. Thus, the Mahalanobis distance criterion function is established to identify the condition and fault pattern of a roller bearing. Practical examples demonstrate that the approach based on Hilbert-Huang transform and AR model can be applied to the roller bearing fault diagnosis effectively.