基于动态模式的转子系统故障诊断

Fault diagnosis of rotor system based on dynamical models

以Jeffcott转子系统基础松动-碰摩耦合故障为例,研宄动态模式的转子系统故障诊断方法.首先,将转子系统正常和故障时的未知系统动态定义为不同的动态模式,对其进行学习,将学到的知识以常数神经网络权值的形式存储,并建立动态模式库;然后将当前被监测转子系统与动态模式库中的动态模式进行比较,根据动态模式的相似性定义,依据最小误差原则快速判断转子系统与已学过的哪种动态模式相似,实现故障的快速检测与分离.仿真结果验证了算法的有效性.

Taking pedestal looseness and rub-impact coupling fault of a Jeffcott rotor system as an example,we investigate fault diagnosis based on dynamical model for rotor system.First,the unknown dynamics of rotor system in normal and fault conditions are defined as different dynamical models which will be learned.The learned knowledge of the approximated system dynamics is stored in constant neural networks,and the dynamical model bank is established.Second,by comparing the set of dynamical models with the monitoring rotor system,a set of recognition errors are generated,which are taken as the similarity measure between the dynamics of the learned dynamical models and the monitoring rotor system.Therefore,the dynamics of the current system similar to one of the learned dynamics can be rapidly recognized according to the smallest error principle,so that faults can be detected and isolated quickly.Simulation results show the effectiveness of the proposed method.