Application of Fuzzy Bayesian Network in Reliability Assessment of Turbofan Engine Startup System
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摘要: 飞机涡轮风扇发动机的组成结构极其精密复杂,且启动系统的健康状态对发动机的影响尤为重大,因此对发动机启动系统的可靠性评估不管是对航空工程的安全性和稳定性,都有着举足轻重的作用。为提高系统可靠性分析的准确性,将原始故障数据进行梯形模糊处理,并结合专家经验进行两种重要度分析和后验概率计算,完成对涡扇发动机启动系统可靠性评估的建模。通过实例验证,对某型航空涡扇发动机的启动系统进行可靠性评估,计算系统中各组件的不同状态对启动系统产生的影响及其重要程度,找出系统易出故障的薄弱环节,为提高整个发动机系统的安全性和可靠性寻找思路与方法。Abstract: The structure of aircraft turbofan engine is extremely precise and complex, and the health of the engine's starting system has a particularly important impact on the engine. Therefore, the reliability evaluation of the engine's starting system plays a vital role in the safety and stability of air flight. In order to improve the accuracy of system reliability analysis, trapezoidal fuzzy method is used to process the original fault data. Based on experts′ experience, two kinds of importance and the posterior probability are calculated, which complete the modeling of reliability assessment of turbofan engine starting system. By means of example verification, the reliability evaluation of the starting system of a certain type of aero-turbofan engine is carried out, the influence of different states of each component on the starting system and its importance degree are calculated. So we can find the weak links of the starting system that are easy to fail, which can supply us ideas and methods to improve the safety and reliability of the whole engine system.
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Key words:
- fuzzy number /
- bayesian network /
- importance /
- turbofan engine starting system /
- reliability analysis
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表 1 BN各节点及名称
节点 名称 节点 名称 x1 电机轴承故障 x8 转换器失灵 x2 电机线路烧蚀 x9 转换器烧蚀 x3 电机线路断路 y1 启动电机故障 x4 调压器烧蚀 y2 调压器故障 x5 调压器失灵 y3 电源转换器故障 x6 调压器线路断路 T 启动系统故障 x7 电源电量故障 表 2 根节点故障概率及模糊子集
根节点xj 故障率 梯形模糊数 x1 0.004762 (0.000333,0.000429,0.000524,0.000619) x2 0.232558 (0.162791,0.209302,0.255814,0.302325) x3 0.125000 (0.087500,0.112500,0.137500,0.162500) x4 0.004500 (0.003150,0.004050,0.004950,0.005850) x5 0.006135 (0.004295,0.005522,0.006749,0.007976) x6 0.008321 (0.005825,0.007489,0.009153,0.010817) x7 0.005545 (0.003882,0.004991,0.006100,0.007209) x8 0.001523 (0.001066,0.0013719,0.001675,0.001980) x9 0.027734 (0.019414,0.024961,0.030507,0.036054) 表 3 中间节点
${y_1}$ 的条件概率表编号 x1 x2 x3 y1 0 0.5 1 1 0 0 0 1 0 0 2 0 0 0.5 0.4 0.4 0.2 3 0 0 1 0 0 1 4 0 0.5 0 0.4 0.5 0.1 5 0 0.5 0.5 0.2 0.2 0.6 6 0 0.5 1 0 0 1 7 0 1 0 0 0 1 8 0 1 0.5 0 0 1 9 0 1 1 0 0 1 10 0.5 0 0 0.4 0.4 0.2 11 0.5 0 0.5 0.2 0.4 0.4 12 0.5 0 1 0 0 1 13 0.5 0.5 0 0.1 0.3 0.6 14 0.5 0.5 0.5 0.1 0.1 0.8 15 0.5 0.5 1 0 0 1 16 1 0 0 0 0 1 17 1 1 1 0 0 1 表 4 根节点状态重要度
根节点 状态重要度 $I_{0.5}^{De}({x_j})$ $I_1^{De}({x_j})$ x1 0 0.035723 x2 0.020630 0 x3 0 0 x4 0 0.166587 x5 0 0.030000 x6 0 0 x7 0 0 x8 0 0 x9 0 0 表 5 根节点模糊重要度
根节点 模糊重要度 $I_{0.5}^{Fu}({x_j})$ $I_1^{Fu}({x_j})$ x1 0.016845 0.134435 x2 0.035370 0.155440 x3 0.021635 0.150990 x4 0.020655 0.142245 x5 0.028445 0.116225 x6 0.027460 0.138510 x7 0.031300 0.123485 x8 0.035770 0.126825 x9 0.027215 0.130855 表 6 根节点后验概率
根节点 后验概率
$P({x_j} = 1|T = 1)$根节点 后验概率
$P({x_j} = 1|T = 1)$x1 0.00049 x6 0.00858 x2 0.23138 x7 0.00572 x3 0.12836 x8 0.00157 x4 0.00400 x9 0.02857 x5 0.00632 -
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