Reliability Evaluation Of Mechanical System Based On Bayesian Network

Doing a mass of system reliability experiments on complicated machinery system is impossible because of considerable expenses and experiment organization. How to make the best of components and system's experiment information to evaluate complicated machinery system accurately is a complicated problem. Now reliability block-diagram method, fault tree method and Monte-Carlo simulation method etc.are the usual methods of evaluating machinery system reliability.For the some limits of these methods, the more suitable reliability evaluation method needs to be researched.The traditional models for reliability analysis are usually under the binary assumption for each element and the entire system, i.e. system and all elements are considered as being nothing but perfect functioning state and complete failure state, express the working states by means of two-value logic, less considering the multi-state and partly failures of all elements, which can lead to the partly failure of the entire system. Such as valve system, normal working,blocking up,seeping, switch failure etc.are all its states and also system can perform their tasks with various distinguished levels of efficiency usually referral to as performance rates. So only research system's two states to bulid a reliability model is not agree with the real case.Dependence is the common characteristic of system failure, it is an important cause leading to the system failure dependence. Omitting the failure dependence, qualitative analysis and quantitative calculating simply under independence hypothesis condition will lead to a large error. Probability hazard analysis shows that failure dependence is one of the main reasons to make the system and equipment failure, such as it is the main reason to make the nuclear power plant failure. So it is quite important to pay much more importance to failure denpendence.Bayesian networks provide a method to represent knowledge in a graphical mode and can be used to do directed graphical description for causal probability relation between random variables. They are mainly used for uncertainty knowledge representation, casual inference and diagnosis inference. By means of various inference modes, the weak elements of system can be readily identified. Bayesian networks makes the relations of components in system more direct and clear.Applying Bayesian networks on reliability evaluation of machinery system and analyzing the multi-state system and common cause system are the keypoints in the thesis.Taking the advantages of Bayesian networks, the author makes a deep study on applying Bayesian networks to machinery system reliability assessment. The main contributions are:Applying Bayesian networks on machinery system, eapecially complicated machinery system and building machinery system reliability model based on bayesian networks after analyzing the characters of Bayesian networks. The model can monitor the uncertainty variables, calculate the work probability and conditional failure probability, such as one or more components' conditional failure probability as the system fails, inference and diagonosis the system, find out the weak elements of the sytem.Applying bayesian networks on multi-state machinery system based on the application of Bayesian networks on machinery system.and building multi-state machinery system reliability model based on Bayesian networks through analyzing and confirming Bayesian networks. It is direct and simple to apply the model on multi-state machinery system evaluation which escaping the calculation of Minimal route-set and cut-set. The multi-state machinery system reliability model don't limit the numbers of components in system that makes the more widely application.Building a failure dependence model of machinery system reliability based on Bayesian networks and applying it on the evaluation of the typical system considering dependence of elements, such as series system, parallel system, k/n(G) system and network system. At the end confirm the validity of the model using Monte-Carlo simulation method.System reliability distribution based on Bayesian networks model is researched.By analyzing and comparing the implication of several kinds of importance approaches used in the reliability analysis of general engineering, the implication of the sensitivity analysis approach for system reliability assessment and the implication of various conditional probabilities inferred from Bayesian networks, it can be concluded that Bayesian networks based approach is more suitable to identify the weak components in a machinery system.