基于威布尔分布和支持向量机的滚动轴承故障诊断方法

提出了一种基于威布尔分布模型和支持向量机的滚动轴承故障诊断方法。首先对滚动轴承原始振动信号建立威布尔分布模型,提取其形态参数和尺度参数构建表征轴承运行状态的特征向量,然后将提取的特征向量输入支持向量机分类器进行故障诊断和识别。分别与基于小波分解和小波包分解特征提取的支持向量机诊断方法进行滚动轴承故障试验仿真比较,结果表明,基于威布尔分布模型特征提取的支持向量机诊断方法具有更高的故障识别准确率。

Fault Diagnosis of Rolling Bearings Based on Weibull Distribution and Support Vector Machine

A novel approach to fault diagnosis of rolling bearings based on Weibull distribution model and support vector machine is proposed. Firstly, Weibull distribution model for original vibration signal of rolling bearings is set up, and its shape parameters and scale parameters are extracted. Then the extracted feature vectors are transmitted to the classifier of support vector machine for fault diagnosis and recognition. It is compared to the common feature extraction methods based on wavelet decomposition and wavelet packet decomposition. The experimental simulation results show that the proposed method has the higher accuracy for fault recognition.