Research On Intelligent Algorithmm Of System Level Fault Diagnosis

In today’s world,with the development of economy and the progress of science and technology,the scale of computer system gradually increases,and the probability of system failure also increases.Therefore,the reliability and security of protection system become more and more important.An important means to improve the reliability of the system is to use an effective diagnosis algorithm to quickly locate the fault in the system and accurately judge the system fault set,so that the programmer and maintenance personnel can repair the fault as soon as possible,so that the system can return to the normal working state before the fault occurs.System level fault diagnosis is an effective method to detect system faults.This system level fault diagnosis method is relatively simple and less expensive,but the analysis and test results are more and more complex,so it becomes a very meaningful work to find a good diagnosis algorithm.At present,some intelligent algorithms have some limitations,which can not solve the problem of system level fault diagnosis.In this paper,bat algorithm and particle swarm optimization algorithm are improved to solve the system level fault diagnosis problem under Malek model and Chwa & Hakimi model.In this paper,the research content of system level fault diagnosis intelligent algorithm includes the following two aspects:(1)Bat algorithm is a new swarm intelligence algorithm.Compared with other intelligent algorithms,bat algorithm needs less parameters,has higher diagnosis accuracy,lower time complexity,good generalization ability,better global optimization ability,simpler mathematical model and higher calculation efficiency.However,the convergence accuracy of the algorithm is low and it is easy to fall into prematurity.In view of the above characteristics,this paper proposes a bat algorithm for system level fault diagnosis based on genetic factors under Malek model.The genetic competition mechanism is introduced into the optimization algorithm to solve the premature problem and improve the local search performance.The simulation results show that the improved algorithm can quickly and accurately solve the problem of system level fault diagnosis under Malek model.(2)A system level fault diagnosis particle swarm optimization algorithm based on ant colony information mechanism in Chwa & Hakimi model is proposed.The algorithm is mainly divided into three parts: in the Chwa &Hakimi model,the initial population is generated by the combination of fault free diagnosis method and absolute fault basis idea;then the fitness function with equation constraints is designed;finally,in order to prevent the algorithm from low efficiency in the early stage,premature convergence in the later stage,imbalance between global search and local search,the particle swarm optimization algorithm is added The pheromone mechanism of ant colony algorithm is proposed.The simulation results show that the algorithm has good global search ability and fast convergence speed,which highlights the effectiveness of the algorithm.