基于多重分形降趋算法与改进的K均值聚类滚动轴承故障诊断

针对滚动轴承振动信号非线性、非平稳的特点，提出采用多重分形降趋算法计算多重分形谱参数作为特征参数，对比分析了多重分形降趋波动分析法及多重分形降趋移动平均法提取轴承故障特征的优劣性.并提出改进的K均值聚类分析对多重分形降趋算法提取的特征参数进行分类，从而实现轴承故障诊断的目的.运用滚动轴承公开数据对方法进行验证，提取时域特征与多重分形谱参数进行对比分析，并对两种多重分形降趋算法的效果进行对比分析，验证了多重分形降趋波动分析法与改进K均值聚类相结合对轴承故障诊断的有效性，为轴承故障诊断方法提供了一种新的尝试. 

Fault Diagnosis of Rolling Bearing Based on Multifractal Descending Algorithm and Improved K Means Clustering

To improve the nonlinear and non-stationary characteristics of rolling bearing vibration signal, a multifractal descending algorithm was proposed to calculate the multifractal spectrum parameters. Taking the multifractal spectrum parameters as characteristic parameters, the advantages and disadvantages of multifractal descending fluctuation analysis method and multifractal descending moving average method were compared and analyzed for bearing fault feature extraction. An improved K mean clustering analysis was used to classify the feature parameters extracted from the multifractal descending algorithm, so as to realize the purpose of bearing fault diagnosis. The rolling bearing data were used to verify the proposed method, and the time domain characteristics and multifractal spectrum parameters were compared and analyzed. And the effects of two multifractal descending algorithms were compared and analyzed. The results verify the combination effectiveness of multifractal descending wave analysis and improved K means clustering for bearing fault diagnosis, and provide a new attempt for bearing fault diagnosis.