基于多特征提取和LSSVM的轴承故障诊断

Bearing Fault Diagnosis Based on Multi-feature Extraction and LSSVM

针对故障滚动轴承的振动信号具有非线性、非平稳的特点,提出一种基于时域指标、小波包能量和最小二乘支持向量机(LSSVM)的轴承故障诊断方法。分别对滚动轴承的原始信号进行时域分析计算和小波包分解,并提取状态差异较明显的时域指标和小波包分解后能量差异较大的小波包能量作为故障特征向量;将含有多个特征向量的数据样本分为训练样本和测试样本并进行归一化处理;训练样本作为LSSVM的输入来对该模型进行训练,通过训练好的LSSVM模型对测试样本进行分类和诊断。实验结果表明:采用该方法,轴承状态总体识别率为97.5%。

Aimed at the non-linear and non-stationary characteristics of vibration signals of fault rolling bearings,a bearing fault diagnosis method was proposed based on time domain index,wavelet packet energy and least squares support vector machine(LSSVM).The original signal of the rolling bearing was analyzed and calculated in the time domain and decomposed by wavelet packet,and the time domain index with obvious state difference and the wavelet packet energy with great energy difference after wavelet packet decomposition were extracted as fault feature vectors.Data samples contained multiple feature vectors were divided into training samples and test samples and normalized.Training samples were used as the input of LSSVM to train the model,and the trained LSSVM model was used to classify and diagnose the test samples.The experimental results show that the bearing state overall recognition rate is 97.5%.