一种新的复杂网络建模和特征提取方法及应用

Method of Modelling & Feature Extraction Based on Complex Network and Application in Machine Fault Diagnosis

现有复杂网络应用于故障诊断时,通常基于时域信息出发建模,造成信号频域特征缺失,并且提取的网络拓扑特性过于宏观,对网络内部的局部变化不敏感。相比于宏观特征,局部特征往往蕴含更为丰富的信息,能更准确地表征网络模型。针对此问题,提出一种基于频域复杂网络分解的局部特征提取新方法,该方法借助复杂网络的结构特性来获取信号在频域的变化规律,采用对网络局部变化敏感的微观特性表征整个网络模型,不受机理的限制,应用灵活。采用滚动轴承不同故障的数据进行验证,并与常规复杂网络拓扑特征和时域统计参数进行对比分析,结果表明,本研究方法及提取的特征可分性好,对故障识别正确率达99%,可满足滚动轴承故障诊断的需求,同时对其他非平稳信号处理及识别有一定的借鉴意义。

The existing complex network methods are directly from the time domain when it is applied in fault diagnosis,which causes frequency domain information of the signal missing,and the extracted topology features of network are too macroscopic,which is not sensitive to network within the local change.Meanwhile,local features usually have more abundant information and represent the network model more accurately than macro features.As a consequence,a new method of local feature extraction based on frequency complex network decomposition is proposed.The method obtains the changing rule of the signal in the frequency domain with the aid of the structural characteristics of complex networks and uses the microscopic features that is sensitive to network within the local change to represent the whole network.It is flexible to apply without limit by the mechanism.Classification experiments on different faults of rolling bears are conducted to compare the proposed feature,existing complex network topology features and statistical parameters in time domain.The experimental result indicates that the proposed feature has well separability and high recognition efficiency,which could satisfy the need of rolling bearing fault diagnosis and also has a reference value for the non-stationary signal processing of other parts in machine.