基于α稳定分布和支持向量机的轴承模式分类
Pattern Classification for Rolling Bearing Based on α-Stable Distribution and Support Vector Machine

针对滚动轴承发生故障时振动信号表现出来的脉冲特性，提出了一种基于〖WTBX〗α〖WTBZ〗稳定分布和支持向量机的模式分类方法。介绍了〖WTBX〗α〖WTBZ〗稳定分布的定义和概率密度函数，并与故障轴承振动信号的概率密度函数曲线进行比较，证明了具有脉冲特性的轴承振动信号符合〖WTBX〗α〖WTBZ〗稳定分布。用小波包分解技术对不同类型的轴承实测数据进行分解，并提取相应特征参数作为特征向量，建立支持向量机诊断模型，进行特征模式分类。通过与传统的基于峭度和方差的模式分类方法进行比较，表明该方法具有较高的诊断准确性。
A pattern classification method based on α-stable distribution and support vector machine (SVM) is proposed, aiming at the extraction of the pulse characteristics of vibration signals from rolling bearings with faults. First, the α-stable distribution is defined, and its probability distribution function (PDF) is introduced and compared with the PDF of the vibration signals from the faulted bearing. The comparison results confirm that the vibration signals with pulse characteristics agree with the α-stable distribution. Then, the measured signals from different bearings are decomposed by wavelet packet decomposition, and the relevant characteristics parameters are computed and selected as eigenvectors that can be used to classify feature patterns based on SVM. Comparing the presented method with the traditional method illustrates its better classification performance.