基于半马尔可夫模型的民用航空发动机热端组件系统检查间隔优化

陆晓华; 左洪福

doi:10.13224/j.cnki.jasp.2017.12.007

基于半马尔可夫模型的民用航空发动机热端组件系统检查间隔优化

doi: 10.13224/j.cnki.jasp.2017.12.007

南京航空航天大学民航学院，南京 211106

基金项目: 国家自然科学基金（U1533202, 61403198）

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出版历程
- 收稿日期: 2016-09-17
- 刊出日期: 2017-12-28

Optimization of inspection interval for civil aero-engine hotsection subassembly system based on semiMarkov model

摘要

摘要: 根据采集的民用航空发动机热端组件系统检修信息和专家对系统退化状态的判别，在系统状态退化过程为离散半马尔可夫链过程的假设前提下，分别建立了基于专家估计数据、基于检查数据以及基于融合数据的各宏观退化状态驻留时间估计模型，并应用最大似然函数法和MCMC（Markov chain Monte Carlo）法对模型参数进行估计，得到基于不同数据源的各宏观退化状态下驻留时间估计值和状态转移系数，并以一定使用周期内的检修费用最优为目标建立状态转移概率模型，仿真得到3个典型宏观退化状态下的最优检查间隔分别为1750、350、70循环。该仿真结果与目前的民航运行生产工程实际情况非常接近，可以为民航运输企业的检修决策提供客户化的决策支持并提高经济效益。
- 检查决策 /
- 民用航空发动机 /
- 热端组件系统 /
- 半马尔可夫模型 /
- 最优检查间隔 /
- 检修信息
Abstract: According to the acquisition of inspection and maintenance information of civil aeroengine hotsection subassembly system and typical degradation state judgment by experts, the models of sojourn time estimation of each typical macro degradation state based on expert estimation opinion, inspection information and their fusion data were set up under the assumptions of the system state degradation process obeying a discrete semiMarkov chain. The methods of maximum likelihood estimation and MCMC(Monte Carlo Markov Chain) were applied to estimate the model parameters, the sojourn time and the state transition coefficients in each macro degradation state based on these three kinds of data. Meanwhile, the state transition probability models were built in a certain service cycle for optimal inspection and maintenance cost, and then the optimal inspection intervals in three typical macro degradation states, ie 1750, 350, 70cycles were obtained by simulation. The results showed the optimal inspection intervals were relatively close to the reality situations in the civil aviation operation and production, providing the technical supports in the aspect of customeroriented inspection and maintenance decisionmaking and improving economic benefits for civil aviation transport enterprises.
- inspection decisionmaking /
- civil aeroengine /
- hotsection subassembly system /
- semiMarkov model /
- optimal inspection interval /
- inspection and maintenance information

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参考文献(20)

[1]	荆甫雷,王荣桥,胡殿印,等.单晶高温疲劳损伤参量的选取与寿命建模［J］.航空学报,2016,37(9):2749-2756.JING Fulei,WANG Rongqiao,HU Dianyin,et al.Damage parameter determination and life modeling for high temperature fatigue of nickelbased single crystal superalloys［J］.Acta Aeronautica et Astronautica Sinica,2016,37(9):2749-2756.(in Chinese)
[2]	钱惠华,李海,程滔,等.涡轮导向叶片热疲劳分析［J］.航空动力学报,2003,18(2):186-190.QIAN Huihua,LI Hai,CHENG Tao,et al.Thermal fatigue analysis to nozzle guide vanes［J］.Journal of Aerospace Power,2003,18(2):186-190.(in Chinese)
[3]	陈宏杰.基于孔探技术的民航发动机健康管理研究［D］.福建厦门:厦门大学,2014.CHEN Hongjie.Research of the civil aeroengine health management based on borescope technology［D］.Xiamen Fujian:Xiamen University,2014.(in Chinese)
[4]	郭伟,董丽虹,王慧鹏,等.基于红外热像技术的涡轮叶片损伤评价研究进展［J］.航空学报,2016,37(2):429-436.GUO Wei,DONG Lihong,WANG Huipeng,et al.Research progress of damage estimation for turbine blades based on infrared thermo graphic technology［J］.Acta Aeronautica et Astronautica Sinica,2016,37(2):429-436.(in Chinese)
[5]	付旭云,钟诗胜,丁刚.民用航空发动机单元体送修工作范围决策［J］.航空动力学报,2010,25(10):2195-2220.FU Xuyun,ZHONG Shisheng,DING Gang.Civil aeroengine module workscope decisionmaking［J］.Journal of Aerospace Power,2010,25(10):2195-2220.(in Chinese)
[6]	ENDRENYI J,ANDERS G J,LEITE D S A M.Probabilistic evaluation of the effect of maintenance on reliabilityan application to power systems［J］.IEEE Transactions on Power Systems,1998,13(2):576-583.
[7]	JIRUTITIJAROEN P,SINGH C.The effect of transformer maintenance parameters on reliability and cost:a probabilistic model［J］.Electric Power Systems Research,2004,72(3):213-224.
[8]	ANDERS G J,ENDRENYI J,YUNG C.Riskbased planner for asset management［J］.Computer Applications in Power,2001,14(4):20-26.
[9]	DEKKER R,SCARF P A.On the impact of optimization models in maintenance decision making:the state of the art［J］.Reliability Engineering and System Safety,1998,60(2):111-119.
[10]	COOKE R M.Experts in uncertainty:opinion and subjective probability in science［M］.New York:Oxford University Press,1991.
[11]	CHHIBBER S,APOSTOLAKIS G,OKRENT D.A taxonomy of issues elated to the use of expert judgments in probabilistic safety studies［J］.Reliability Engineering and System Safety,1992,38(1/2):27-45.
[12]	WINKLER R L.Uncertainty in probabilistic risk assessment［J］.Reliability Engineering and System Safety,1996,54(2/3):127-132.
[13]	CLEMEN R T,WINKLER R L.Combining probability distributions from experts in risk analysis［J］.Risk Analysis,1999,19(2):187-203.
[14]	MEEKER W Q,ESCOBAR L A.Statistical methods for reliability data［M］.New York:Wiley,1998.
[15]	RAUSAND M,HOYLAND A.System reliability theory:models,statistical methods,and applications［M］.Horwood,England:Wiley,2004.
[16]	APOSTOLAKIS G.Data analysis in risk assessments［J］.Nuclear Engineering and Design,1981,71(3):375-381.
[17]	BIER V.Implications of the research on expert overconfidence and dependence［J］.Reliability Engineering and System Safety,2004,85(1/2/3):321-329.
[18]	MEYER M A,BOOKER J M.Eliciting and analyzing expert judgment:a practical guide［M］.London:Academic Press,1991.
[19]	MOSLEH A,APOSTOLAKIS G.The assessment of probability distributions from expert opinions with an application to seismic fragility curves［J］.Risk Analysis,1986,6(4):447-461.
[20]	DODSON B.The Weibull analysis handbook［M］.2nd.ed.Milwaukee,US:American Society for Quality Quality Press,2006.