New Methods Of Dynamic Fault Tree Analysis Of Complex System And Its Application

Reliability and safety analysis and evaluation of complex systems have becomeone of the hot issues in reliability engineering. Reliability block diagram (RBD), failuremodes, effects and criticality analysis (FMECA), fault tree analysis (FTA), petri netsmethod and Monte Carlo Simulation (MCS) method are the most commonly used toolsfor system reliability analysis. The traditional methods frequently do not consider thedynamic characteristics of system failure, such as the sequential dependency ofcomponent failure. However, in actual complex engineering systems, component failureevents are mostly not independent to each other, but there are many interacting dynamiccharacteristics. On the other hand, due to the lack of data, the factors such as the updateof product design, human factors, et al. will cause the uncertainty of components failuredata. At present, the fault tree analysis considering the dynamic characteristics of failurehas achieved fruitful results. However, the research of fault tree analysis whichconsidering the influence in combination of fuzzy uncertainty and dynamic failurecharacters is still insufficient. Therefore, it is necessary to do some further exploratoryresearches on system reliability analysis on condition that component failures are notindependent and considering fuzzy uncertainty of systems.To solve the above problems, the following works are carried out in thisdissertation:(1) Dynamic fault tree analysis method based on fuzzy Markov model. Markovmodel is a state space method, which can be used for system failure and maintenancemodeling where the failure and maintenance time is exponentially distributed. On thebasis of Markov model and considering the influence of the fuzzy uncertainty ofcomponent failure parameters to system, a research on dynamic fault tree analysismethod in the case of fuzzy failure rate is carried out in this dissertation. A dynamicfault tree model has been built. The triangular fuzzy numbers are used to express thefailure rate of the components and system, after which the fuzzy Markov model hasbeen established based on the dynamic fault tree model obtained before. The fuzzyMarkov model can be solved using the expansion principle of fuzzy theory andLaplace-Stieltjes transformation. The fuzzy failure probability or fuzzy reliability curveon given degree of membership could be obtained. Finally, the fuzzy Markov model based DFTA method is used for reliability modeling and analysis of hydraulic system ofCNC machining center. The results show that this method can conduct reliabilitymodeling and quantitative assessment effectively for systems which have dynamicfailure characteristics and uncertainty of failure rate.(2) Dynamic fault tree analysis method based on Discrete-Time Bayesian Network.The system reliability modeling and evaluation method based on Bayesian Network anddynamic fault tree is studied in this dissertation. In the Discrete-Time Bayesian Networkmodel, the fault tree model of system failure in transformed into a Bayesian Networkmodel, and the logical relationship between the failure components of system isexpressed by the use of Bayesian Network topological structure. Taking advantage ofthe conditional independence of Bayesian Networks, the state space explosion problemfor solving the Markov model corresponding to the dynamic fault tree model can bealleviated. Conditional probability distribution tables for various kinds of logic gates inboth static and dynamic fault trees are created. A solar array drive assembly of satelliteis used for case study. The dynamic fault tree model and corresponding BayesianNetwork model is established, and the junction tree inference algorithm is used forbidirectional probabilistic reasoning for this model. The result shows that this methodcan solve the problem of dynamic complex system reliability analysis and evaluationeffectively.(3) Dynamic fault tree analysis under fuzzy data based on the Continuous-TimeBayesian Network. A system reliability modeling and analysis method based oncontinuous-time Bayesian network is introduced and the fuzzy uncertainty of the systemis also taken into account. The analytical expression of reliability and failure probabilitycan be obtained directly on the basis of Continuous-Time Bayesian Network. Triangularfuzzy number is used to describe the failure rate and construct the fuzzy marginaldensity function and fuzzy distribution function of failure distribution of components.The conditional probability density function and distribution function of non-root nodesfailure events in Bayesian Networks are jointly constructed by the unit step function andimpulse function. Expressions of fuzzy marginal density function and fuzzy distributionfunction for several typical logical gates of fault tree under the fuzzy failure rate dataare derived. The results of a case study verified the feasibility and correctness of thismethod.(4) Dynamic Fault tree analysis method considering common cause failure. The reliability analysis of the instance system with common cause failure is carried out byusing fault tree analysis method. Some classic models and modeling methods forcommon cause failure are introduced. The explicit modeling approach and the squareroot model are used for the fault tree analysis of train rear-end accident. The failureprobabilities of the system with and without considering common cause failure arecalculated, respectively. The result shows that, a large error will exists in reliabilityanalysis result without considering the effect of common cause failure on system. Thisillustrates that common cause failure has very significant impact on the facility securityof transport, and this also provide the foundation of train safety and reliabilityassessment. The dynamic fault tree and Bayesian Network reliability modeling andassessment method considering common cause failure are proposed. The equations fordetermining the conditional probability distribution of spare gate nodes under CCF areestablished. Finally, an example is given to validate the correctness of this method. Thecomparison with MCS shows that the result can meet the requirement of precision.