基于α稳定分布参数估计的滚动轴承故障诊断
Rolling Bearing Fault Diagnosis Based on Parameter Estimate of Alpha-Stable Distribution

针对滚动轴承故障损伤程度难以确定的问题，提出对滚动轴承不同故障位置、不同损伤程度的振动信号进行故障特征提取及智能分类的故障诊断方法。首先，对各状态振动信号进行α稳定分布四参数估计，选取敏感性及稳定性最好的二种参数组成二维故障特征量；然后，输入到经过粒子群优化算法(particle swarm optimization，简称PSO)进行参数优化后的最小二乘支持矢量机(least squares support vectors machine，简称LSSVM)中进行故障[JP2]诊断；最后，通过台架试验数据验证了该方法的有效性，并与未经过PSO参数优化的LSSVM、支持向量机(support vectors machine，简称SVM)方法的诊断结果进行比较。结果表明：该方法可实现滚动轴承故障位置及损伤程度的智能诊断，比未经PSO参数优化的LSSVM、SVM方法具有更优的泛化性，更短的训练、测试时间，可应用于实际工程。
When a rolling bearing fails, it is usually difficult to determine the degree of damage. In light of this problem, a new fault diagnosis method is presented to achieve feature extraction and intelligent classification of different fault positions and degrees of damage of rolling bearing signals. First, the alpha -stable distribution of four parameters of the vibration signals of each status is estimated. Next, the two parameters that are the most sensitive and stable are found and employed as fault feature values. Feature values are regarded as the input of least squares support vectors machine (LSSVM) based on particle swarm optimization (PSO) for judging the fault position and degree of damage of the rolling bearing. Finally, the method′s effectiveness is verified by bench test data, and the method is compared with other related methods. The results show that the presented method can accurately achieve the intelligent diagnosis of the fault positions and degree of damage of rolling bearings, has better generalization than LSSVM or support vectors machine (SVM) methods that are not optimized by PSO, and has the potential to solve practical engineering problems.