For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertaint...For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.展开更多
The propulsion systems of a multi-rotor unmanned aerial vehicle(UAV)is crucial,as it directly affects the UAV’s performance,efficiency,and safety.Since the components of the UAV propulsion system are highly interconn...The propulsion systems of a multi-rotor unmanned aerial vehicle(UAV)is crucial,as it directly affects the UAV’s performance,efficiency,and safety.Since the components of the UAV propulsion system are highly interconnectioned,we developed a fuzzy fault tree analysis method to analysis the varying reliability under different fault conditions.Combining the fuzzy fault tree analysis of the T-S model and the UAV propulsion system model,we constructed a fuzzy fault tree of the T-S type for the system and performed a reliability analysis.This fuzzy fault tree allows us to model the system from two perspectives:fuzzy failure rate and failure degree.Consequently,two methods can be used for failure analysis of UAV systems.The first method involves calculating the system’s fuzzy failure rate based on the component’s fuzzy failure rate.The second method calculates the fuzzy failure rate of the system based on the failure degree of the component.The computational results indicate that both methods are well-suited for fault diagnosis in UAV propulsion systems.Compared to traditional fault tree analysis,which does not subdivide fault degrees,the proposed methods provide more accurate fault rate assessments.展开更多
基金the National Natural Science Foundation of China(51875073).
文摘For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.
文摘The propulsion systems of a multi-rotor unmanned aerial vehicle(UAV)is crucial,as it directly affects the UAV’s performance,efficiency,and safety.Since the components of the UAV propulsion system are highly interconnectioned,we developed a fuzzy fault tree analysis method to analysis the varying reliability under different fault conditions.Combining the fuzzy fault tree analysis of the T-S model and the UAV propulsion system model,we constructed a fuzzy fault tree of the T-S type for the system and performed a reliability analysis.This fuzzy fault tree allows us to model the system from two perspectives:fuzzy failure rate and failure degree.Consequently,two methods can be used for failure analysis of UAV systems.The first method involves calculating the system’s fuzzy failure rate based on the component’s fuzzy failure rate.The second method calculates the fuzzy failure rate of the system based on the failure degree of the component.The computational results indicate that both methods are well-suited for fault diagnosis in UAV propulsion systems.Compared to traditional fault tree analysis,which does not subdivide fault degrees,the proposed methods provide more accurate fault rate assessments.
