基于FFT与CS-SVM的滚动轴承故障诊断

针对滚动轴承故障信号强噪声背景和非线性等特点,为精确识别滚动轴承的故障特征频率并精准分类,提出了一种基于Hanning窗插值快速傅里叶变换并利用布谷鸟算法优化支持向量机的滚动轴承故障诊断新方法。采用Hanning窗对得到的频域信号进行加窗处理并求得样本特征的均方根特征值;经过布谷鸟算法优化后的支持向量机(CS-SVM)对样本数据进行故障诊断分类。通过凯斯西储大学的轴承故障振动信号数据进行的实验,验证了该混合智能诊断方法的有效性和优势,结果表明:所提出的方法可以对轴承故障准确进行分类。

Fault Diagnosis of Rolling Bearing Based on FFT and CS-SVM

In view of the strong noise background and nonlinearity of the rolling bearing fault signal, a new method of rolling bearing fault diagnosis based on Hanning window interpolation fast Fourier transform and using the cuckoo algorithm to optimize support vector machine is proposed to accurately identify the fault characteristic frequency of rolling bearings. The Hanning window is used to deal with the frequency domain signals, and the root mean square eigenvalues of the samples are obtained. The support vector machine(CS-SVM), which is optimized by the cuckoo algorithm, classifying the sample data for fault diagnosis. The effectiveness and advantages of the hybrid intelligent diagnosis method are verified by the test of the bearing fault vibration signal data of Case Western Reserve University. The results show that the proposed method can accurately classify bearing faults.