Research On Fault Diagnosis Of Flexible Manufacturing System Based On Fuzzy Petri Net And Optimization Algorithm

In this paper,we analyzed fault information of flexible manufacturing systems(FMS)and proposed a Asynchronous Variable Learning Factor Particle Swarm Optimization(Asynchronous Variable Learning Factor)based on Fuzzy Petri Net(FPN)and Improved Asynchronous Variable Learning Factor Particle Swarm Optimization(AsyLnCPSO).Firstly,the fault diagnosis significance and application value of the research object are introduced.Then,the working principle,working flow,combined with the schematic diagram of each unit system are explained,and the fault information of the system in the process of work is extracted and summarized.Then the fault information of FMS is modeled by FPN.Then the basic theory of Petri net is discussed in detail.We also introduced the improved Asylum AsyLnCPSO optimization algorithm.Then improved AsyLnCPSO optimization algorithm of particle characteristics and corresponding parameter mapping of FPN fault diagnosis,and by using the classical test functions with the classic optimization algorithm are compared,and the simulation to verify the improved AsyLnCPSO optimization algorithm has obvious advantages in classical optimization algorithm,and the algorithm of the particles and the parameters of the FPN mapping.Finally,we combined Asylum AsyLnCPSO optimization algorithm with fuzzy Petri net and applied it to the FMS.Through simulation analysis,the results showed that the fault diagnosis method could improve the accuracy of FMS effectively and was suitable for the fault diagnosis of FMS in this paper.