基于多特征提取的滚动轴承故障诊断方法

滚动轴承故障是旋转机械失效和损坏的最主要原因之一。轴承振动信号通常表现为非线性和非稳态的特征。常规的时域和频域方法不容易对轴承工作的健康状况做出准确的评估。提出了一种基于多特征提取的滚动轴承故障检测方法，首先从轴承振动信号中提取故障特征（熵特征、Holder系数特征及改进分形盒维数特征），然后通过灰色关联理论算法自动地识别出轴承的故障类型和严重程度。该方法能够在确保检测实时性的同时，准确有效地识别不同的滚动轴承故障类型及其严重程度。

Study on Rolling Bearing Fault Diagnosis Based on Multi-dimensional Feature Extraction

The failure of rolling bearing is the foremost cause of the failure and breakdown of rotating machines.As the rolling bearing vibration signal is of nonlinear and nonstationary characteristics,using common time domain or frequency domain approaches cannot easily make an accurate estimation for the rolling element bearing healthy condition.A rolling bearing fault diagnostic approach based on multi-dimensional feature extraction is proposed.Firstly,a multi-dimensional feature extraction algorithm on the basis of entropy,Holder coefficient and improved generalized box-counting dimension theories is proposed for extracting health status feature vectors based on the bearing vibration signals,and secondly a gray relation algorithm is employed for achieving accurate estimation of different fault types and different severities intelligently using the extracted feature vectors.The approach can efficiently and effectively recognize different fault types and different severities in comparison with the existing artificial intelligent methods,and can be suitable for on-line health status monitoring.