Reliability Analysis And Assessment Of Complex System Under Epistemic Uncertainty

With the increasing complexity and larger size of modern advanced engineering systems,the traditional reliability analysis and evaluation technology which is based on a large number of sample data can not meet the demand of complex system.Especially for the complex systems which have stringent reliability requirements,such as aviation,aerospace,power and nuclear power plants systems.The limitations of experimental conditions,the randomness of measured data,the complexity of system structure and the differences in cognitive abilities,which have made the uncertainties been introduced into the engineering system reliability analysis.As well as the further understanding of system failure mechanism and physics developed continuously,it has been observed that the reliability analysis methods based on binary theory has already cannot reflect the relationship between component performances and system performances.At the same time,due to the increased redundancy system structure,the contribution of independent failure to system has decreased and,the dependent failure of multiple components has become a common feature of system failure.Therefore,when using the traditional reliability theory to conduct the reliability modeling,analysis and assessment of complex system,it cannot reflect the complexity character of this kind of systems,and cannot meet the engineering needs.Aimed at the engineering application requirement of reliability analysis and evaluation of complex system,this dissertation carries on a systemic research,including components states definition,structure analysis,reliability analysis and life assessment.The reliability analysis and evaluation of complex system,when multi-state properties,epistemic uncertainties,common cause failures(CCFs)and dynamic properties are discussed.A theoretical framework on system reliability analysis and evaluation based on complexity system has been established.The primary research contributions and innovative outcomes are summarized as follows:(1)Reliability analysis of complex system under epistemic uncertainty based on belief universal generation function(UGF)method.Because of the complexity of engineering systems,and the fact that insufficient data are only available to obtain the precise state probability of components,an extended UGF based on D-S evidence theory(belief function theory)and interval theory is introduced in this paper to conduct the reliability analysis of multi-state systems(MSSs)with epistemic uncertainty.The behavior of CCFs is further incorporated,and the occurrence probability of CCFs is evaluated using a weighted impact vector method.The proposed method has applied to an computer numerical control(CNC)machine system.The case study shows that the belief UGF method can effectively avoid the interval expansion problem and the overestimation problem involved in the interval UGF method,and the proposed method can be used to provide a reliable way to evaluate the reliability of MSSs with interval data and CCFs.(2)Reliability analysis of complex MSS with CCF based on interval-valued fuzzy Bayesian network(BN).Due to the diversity of input information and the system failure factors,it is often difficult to get sufficient data to obtain the accurate failure probabilities of basic events in complex system.In consideration of the epistemic uncertainty caused by lack of probability statistical information,the fuzzy theory is employed to express the fuzzy information of system,and the basic events failure probabilities are described by interval-valued fuzzy numbers.Taking account of the influence of CCF to system reliability and the widespread presence of MSS in engineering practices,a method for reliability modeling and assessment of a MSS with CCF based on interval-valued fuzzy BN is proposed by taking the advantage of graphic representation and uncertainty reasoning of BN.The model is applied to an engineering system to demonstrate its effectiveness and capability for directly calculating the system reliability on the basis of multi-state probabilities of components.(3)Reliability evaluation of complex MSS with multiple CCF groups based on belief BN.Based on the BN method for reliability analysis of MSS,the evidence theory is employed for the state space reconstruction of MSS,an uncertain state which used to express the epistemic uncertainty is introduced in the new state space.The integration of evidence theory with BN is achieved by updating the conditional probability tables.When the multiple CCF groups are considered in complex redundant system,a modified ? factor parametric model is introduced to model the CCF in system,and an evidence theory based BN method is proposed for the reliability analysis and evaluation of complex multi-state system.The reliability analysis of feeding control system for CNC heavy-duty horizontal lathes by this method has shown that the present method has high computational efficiency and strong practical value.(4)Comprehensive reliability assessment of complex system under epistemic uncertainty based on BNs and Monte-Carlo(MC)simulations.The complexity of the system structure and failure mechanism makes it more difficult for reliability assessment of these systems.Uncertainty,dynamic and nonlinearity characteristics always exist in engineering systems due to the complexity introduced by the changing environments,lack of data and random interference.In view of the dynamic characteristics within the system,it makes use of the advantages of the dynamic fault tree(DFT)for characterizing system behaviors.An extended probability-box(P-Box)is proposed to convey the present of epistemic uncertainty induced by the incomplete information about the data.By mapping the DFT into an equivalent BN,relevant reliability parameters and indexes have been calculated.Furthermore,the MC simulation method is utilized to compute the DFT model with consideration of system replacement policy.The analysis of example complex electromechanical system indicates that this integrated approach is more flexible and effective for assessing the reliability of complex dynamic systems.