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 In order to solve the uncertainty problems in engineering diagnosis, a threelayered topological model using Bayesian diagnostic network was proposed based on condition, operation, fault, and symptom. Moreover, it used the superiority of Bayesian network in dealing with uncertainty and experts synthetically experience. The model represented the complicated relationships in engineering diagnosis intuitively. A practical example shows that its evidences for diagnosis are more sufficient and its... In order to solve the uncertainty problems in engineering diagnosis, a threelayered topological model using Bayesian diagnostic network was proposed based on condition, operation, fault, and symptom. Moreover, it used the superiority of Bayesian network in dealing with uncertainty and experts synthetically experience. The model represented the complicated relationships in engineering diagnosis intuitively. A practical example shows that its evidences for diagnosis are more sufficient and its result of network inference is more consistent with practice in comparison with naive Bayesian networks, because it takes account of operating actual conditions and operation records of equipment. This model has been used successfully for diagnosis of an industrial gas turbine in a refinery of a petrochemical complex.  为了解决工程诊断中的不确定性,提出了一种基于工况操作故障征兆的3层拓扑结构的贝叶斯诊断网络模型.该模型发挥了贝叶斯网络解决不确定性问题的优越性,融合了专家的经验知识,用图形化方式直观地表达了诊断中的复杂关系.应用实例表明,与传统质朴型贝叶斯诊断网络相比,该模型考虑了诊断对象的实际运行工况和操作情况,用于诊断的证据信息更充分,网络的诊断推理结果更符合诊断实际,已成功地应用于某石化公司炼油厂的烟机诊断中.  Bayesian Network is a powerful tool used to express and infer uncertain knowledge. The network inference is its important content. This paper proposes an approximate algorithm, in which the stochastic sampling process is based on a random number generator and a roulette wheel. The roulette wheel determines the node states according to its prior probabilities. When the amount of sample serials is enough, margin and condition statistic quantity are close to margin and condition probability of... Bayesian Network is a powerful tool used to express and infer uncertain knowledge. The network inference is its important content. This paper proposes an approximate algorithm, in which the stochastic sampling process is based on a random number generator and a roulette wheel. The roulette wheel determines the node states according to its prior probabilities. When the amount of sample serials is enough, margin and condition statistic quantity are close to margin and condition probability of node respectively. Then approximate inference results of network are obtained. Numerical simulating results show its effectiveness. The algorithm has been used for Bayesian Diagnostic Network in Tianjin Petroleum Chemical Complex.  贝叶斯网络是一种强有力的不确定性知识表达和推理工具。网络的推理是贝叶斯网络的重要内容之一。该文提出了一种近似仿真算法。由随机数发生器产生随机数,并按节点的先验概率,由赌轮对网络各个节点状态赋值,得到一个采样样本序列。当样本序列的数量足够大时,边缘统计量和条件统计量与节点的边缘概率和条件概率接近,从而得到网络的近似推理结果。仿真结果表明,该算法与精确解接近,有较好的适应性。基于该算法构造的贝叶斯诊断网络系统已成功应用于天津石化炼油厂。  The fact that Bayesian diagnostic network must be constructed for a specific diagnosed object in the practical applications motivated to develop a new network platform. The number pattern was introduced to describe the topological model of network, and relation matrix was adopted to represent those relationships among nodes, thus the platform provided an effective and flexible operation environment for construction and reasoning of Bayesian diagnostic network. The three important problems... The fact that Bayesian diagnostic network must be constructed for a specific diagnosed object in the practical applications motivated to develop a new network platform. The number pattern was introduced to describe the topological model of network, and relation matrix was adopted to represent those relationships among nodes, thus the platform provided an effective and flexible operation environment for construction and reasoning of Bayesian diagnostic network. The three important problems for realization of the platform were discussed: digitization of network, identification of topological order and selection of roulette wheel. A Bayesian diagnostic network for a gas turbine was constructed under the proposed platform. The practical example confirms the efficiency of the platform to construct and reason Bayesian diagnostic network in the engineering applications.  针对贝叶斯诊断网络在实际应用时需要对具体对象建立相应诊断网络的问题,开发了一个贝叶斯诊断网络平台.重点讨论了网络的数字化、网络拓扑顺序的确定和赌轮选择等平台实现的3个关键问题.采用数字形式来描述网络的拓扑模型,刻画了网络的全部信息.节点间的关系则用关系矩阵简洁、直观地予以表达,并据此确定网络的拓扑顺序.利用赌轮的选择功能,实现了网络节点状态的实例化.该平台简单易用,为网络的建立和推理提供了一个有效、便利、通用的运行环境.利用该平台为天津石化炼油厂的一台烟机建立了贝叶斯诊断网络,实例表明,该平台能够用于贝叶斯诊断网络的构建和推理,也为贝叶斯诊断网络的工程应用提供了一个有力的工具.  
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