时域流形特征增强在数控机床轴承故障诊断中的应用

Application of Time Domain Manifold Feature Enhancing in Fault Diagnosis of CNC Machine Tool Bearings

以数控机床轴承的时域振动信号为研究对象,提出一种基于流形学习的特征增强方法。首先,将采集信号的时间序列进行相空间重构,通过计算子相空间的信息熵来构建信号在特征空间中的表示,并以流形距离作为原始信号来集中不同故障类型的度量。然后,使用等距特征映射算法求取信号在特征空间中同胚的低维流形,其结果可用于对故障类型的分类判别。经实例数据集的验证分析发现,信息熵—等距特征映射变换能够在低维特征空间表达并强化轴承时域信号的故障类型特征,可有效应用于数控机床轴承单一和复合故障场景的设备运行诊断。

Based on the time domain vibration signals of CNC machine tool bearings,a feature enhancement method based on manifold learning is proposed.The time series of collected signals are reconstructed in phase space,and the information entropy of different sub-manifolds is calculated to construct the representation of the original signal in the feature space.The manifold distance in the feature space is used as a measure of different types of faults in the original signal set.By using the Isometric Feature Mapping(ISOMAP)algorithm,while retaining the information of fault types,the isomorphic low-dimensional manifolds of signals in the feature space are obtained for fault type classification.Through the verification analysis of example data sets,it is shown that the information entropy-ISOMAP transformation can express and enhance the type features of bearing faults in the low-dimensional feature space,and can be effectively applied to diagnose single and compound fault scenarios of CNC machine tool bearings.