基于频段能量特征的低速滚动轴承故障诊断

Low Speed Rolling Bearing Fault Diagnosis Based on Frequency Bands Energy Features

直升机自动倾斜器滚动轴承通常在低速环境下工作,其故障特征频率易淹没在各种干扰频率中,因此以特征频率处的谱峰作为故障特征的传统诊断方法效果不佳.为此提出一种基于频段能量特征的低速滚动轴承故障诊断方法.计算振动信号的FFT和功率谱,利用轴承正常、内圈故障、外圈故障和滚珠故障4种状态的振动信号在功率谱上能量分布不同的特点,构建故障特征向量.利用具有小样本优势的支持向量机构建分类器,进行故障类型诊断.在实验台上模拟直升机自动倾斜器滚动轴承的低速工作环境,进行了故障模拟实验,采集振动数据,与传统LMD包络谱特征方法进行对比,验证了该方法在低速滚动轴承故障诊断方面的适应性和优越性.

When the rolling bearing of a helicopter swash plate works in a low speed environment, the characteristic frequency of faults completely submerged in various inter- ferences, making the traditional diagnosis method depending on spectral peaks of char- acteristic frequencies ineffective. To solve the problem, a low speed rolling bearing fault diagnosis method based on frequency bands energy features is proposed. FFT and power spectrum of the fault signal are calculated. Energy distribution characteristics in the sig- nals of four kinds of vibrations, i.e., normal, inner ring, outer ring and ball fault, are used to construct a fault feature vector. Finally, support vector machine （SVM）, which can use small scale samples, is used to construct a classifier used for determining the type of fault. On an experimental platform, simulation test was carried out in a low speed working envi- ronment of rolling bearing of a helicopter swash plate. Analysis of the collected vibration signals shows that, compared with the traditional LMD and envelope spectrum character- istics method, the adaptability and superiority of the proposed method in low speed rolling bearing fault diagnoses are verified.