基于单层SAE与SVM的滚动轴承性能退化评估

Performance Degradation Assessment for Rolling Bearing Based on Single Layer SAE and SVM

滚动轴承是旋转机械设备的常用关键部件之一,其性能退化评估是机械设备状态监测与视情维修的基础和依据。为及时准确掌握滚动轴承性能退化趋势与程度,提出基于单层稀疏自编码学习和支持向量机的滚动轴承性能退化评估方法,研究能够深度挖掘数据各种潜在隐含信息的稀疏自编码学习方法以及基于时频域特征和稀疏自编码学习的轴承状态特征的提取方法;提出基于支持向量机分类算法改进的轴承性能退化评估算法,并应用到滚动轴承的性能退化评估模型中,确定了模型参数寻优的方法;最后将所获得的轴承状态特征输入到轴承性能退化评估模型,得到了轴承性能退化趋势图,并通过滚动轴承实例验证了所提出方法的实用性。

Rolling bearing is one of the key components of rotating mechanical equipment,which performance degradation assess-ment is the basis of condition monitoring and condition-based maintenance of mechanical equipment.A method of evaluating the per-formance degradation of rolling bearing based on single layer sparse autoencoder(SAE)and support vector machine(SVM)was pro-posed in order to accurately grasp the degradation trend and the degree of performance of rolling bearing.Sparse autoencoder learning method by which the potential implicit information of data could be deeply exploited and the extraction method of bearing state feature based on time-frequency domain and sparse autoencoder were studied.An improved evaluation algorithm of bearing performance degra-dation based on SVM classification algorithm was proposed and applied to the performance degradation evaluation model of rolling bear-ing.The method of model parameter optimization was determined.Finally,the performance degradation trend of the obtained feature input performance degradation evaluation model was obtained and an example of rolling bearing was given to demonstrate the practica-bility of the proposed method.