寿命预测
2015-07-26 11:41:00 27 举报
AI智能生成
寿命预测是一种通过分析个体的年龄、健康状况、生活方式等因素,来预测其可能的寿命长度的方法。这种方法通常使用在医学研究中,以帮助医生和研究人员更好地理解人类的生命周期,并找出影响寿命的各种因素。然而,需要注意的是,寿命预测并不是一种绝对准确的科学,它只能提供一个大致的预期范围,而实际的寿命长度可能会受到许多不可预见的因素的影响。尽管如此,寿命预测仍然是一种有价值的工具,可以帮助我们更好地规划我们的生活,以及对待我们的生命。
作者其他创作
大纲/内容
寿命预测
研究意义
重大装备发生事故造成危害大
事故例子
维修难,过早更换零件容易造成浪费
维修难,零配件大多需要特制,准备时间长,价格贵
零件例子,火箭外壳,空间站
可靠性估计难,样本少,环境复杂且恶劣,载荷多变
研究对象
飞行器,舰船,火箭,大型工程机械,发电机组
领域
发电设备,航空航天,石油化工,汽车,铁路运输,数控加工,冶金,武器装备
汽车领域内容
汽车零部件,运货车箱的端梁,运货车厢,汽车车轮,汽车发动机,汽车齿轮
方法:时域波再现技术
张帆,靳晓雄.时域波形再现技术在汽车零部件疲劳寿命预测中的应用[J】.汽车科技,2007(1):21-23. ZHANG Fan。fiN Xiaoxiong.The application of time waveform replication technology in automobile compocation technology in automobile component fatigue life prediction[J].A
神经网络
】苏春华,罗雷,海军,等.基于BP神经网络的汽车发动机寿命预测【J丁.军事交通学院学报,2009,II(4):49-51,70.SU Chunhua,LU0 Lei,HAI Jtm,et a1.The predictionof vehicle’S service life with BP neural network[J]. Joumal ofAcademy ofMilitary Transportation,2009,11(4):49·51,70.
灰度系统
】于雷.灰色系统理论在汽车齿轮寿命预测中的应用[J】. 汽车技术,2006(9):24-26.YU Lci.Application of gray system theory in predicting li岛of automobile gear[J].Automobile Technology, 2006(9):24-26.
发展历史
技术开创期1847
发展期189x-190x
技术完善期
发展趋势,热点,难点
现在都是单一寿命预测,距离整体寿命预测较远
现有研究方法
力学方法
信息技术
累积疲劳损伤理论
线性累积损伤理论
palmgren-miner
双线性累积损伤理论
grover-manson
非线性疲劳累积损伤理论
corten-dolan
marco-starky
henry
参考文献两篇
FATEMI A,YANG L.Cumulative fatigue damage andlife prediction theories:A survey of the state of the art for homogeneous materials[J].International Journal of Fatigue,1998,20(1):9-34.\u00A0
CH:ABOCHE J L,LESNE PM.A non-linear continousfatigue damage model[J].Fatigue&Fracture ofEngineering Materials&Structures,1988,1l(1):1-17.\u00A0
基于断裂力学的疲劳扩展理论
损伤力学
能量法
概率统计方法
人工智能
相关文献
】MOHANTY J R,VERMA B B,RAY P K,et a1.Application of artificial neural network for fatigue life prediction under interspersed mode一1 spike overload[J] Journal ofTesting andEvaluation,2010,38(2):96-101.
BEZAZlA,PIERCE SG,WORDEN K,cta1.Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network[J].International Journal of Fatigue,2007,29(4):738—747.
KWON Y I,LIM B S.A study of creep-fatigue lifeprediction using an artificial neural network[J].Metals and Materials International,200l,7(4):3 ll一3 17.\u00A0
LI J W,PENG Z F.Artificial neural network predictionof creep rupture life of nickel base single crystal super'alloys[j].Aeta Metallurgica Sinica,2004,40(3):257-262.
】LIAO X L,XUW F,GAO Z Q.Application ofartificial neural network to forecast the tensile fatigue life of ·carbon material[C]H 7th International Conference on Fracture and Damage Mechanics,September 9-1 1,2008,Seoul,Korea.Stafa-Zurich:Trans.Tcch.Publications L1rD,2008:533.536.
】NATARAJAN U,PER/ASAMY VM,SARAVANAN&Application of particle swarln optimisation in artificial neural network for the prediction of tool life[J]. International Journal of Advanced ManufacturingTechnology,2007,31(9-10):871-876.\u00A0
】PLEUNE T T,CHOPRA O IC Using artificial neural networks to predict the fatigue life of carbon and low·alloy steels[J].Nuclear Engineering and Design,2000,197(1—2):1-12.
】SEKERCIOGLU T,KOVAN v.Prediction of static shear force and fatigue life of adhesive joints by artificial neural network[J].Kovove Materialy-Metallic Materials,2008,46(1):51—57.\u00A0
SOHN I,BAE D.Fatigue life prediction ofspot—weldedjoint by strain energy density factor using artificial neural network[C]//4th International Conference Oll Fracture and Strength ofSolids,August 16-18,2000。 Pohang,Korea.Stafa-Zurich:Trans.Tech.PublicationsUr】),2000:957.962.\u00A0
】Slu[NIVASAN VS,NAGESHAA,Ⅵ也SAN M,et a1. Artificial neural network approach to low cycle fatigue and creep-fatigue interaction life prediction of modified 9CrolMo ferritic steel[J].Transactiom of the Indian Institute ofMetals,2005,58(2.31:261.267.\u00A0
】SRNI、伪SANVS,Ⅵ虬SANA,RAO KB S,etal.Low cycle fatigue and creep-fatigue interaction behavior of 316L(N)stainless steel and life prediction by artificial neural network approach[J].International Journal of Fatigue,2003,25(12):1327-1338.\u00A0
VAssl0POuLOS A P, GEORGoPOU】LoS E F。DIONYSOPOULOS V Artificial neural networks inspectrmn fatigue life prediction of composite materials[J].International Journal of Fatigue,2007,29(1):20—29.\u00A0
分类
back propagation multi-layer network
radial basis functions network RBF
对比文献
Review and application of Artificial Neural Networks models\t in reliability analysis of steel structures
专家系统
ESPASA M L,GAUDES J,PRUENCA N,et a1.Expertsystem to life prediction of petroleum refinery piping. High temperature[J].Afinidad,1997,54(468):98·102.\u00A0
模糊系统
YAN J H,ISOBE N,LEE J.Fuzzy logic combined logistic regression methodology for gas turbine first-stage nozzle life prediction[C]//6th International Conference on e-Engineering and Digital Enterprise Technology,August 26-29。2007,Harbin,China.Stafa.Zurich:Trans.Tech.Publications LTD,2008:
进化算法
】BUKKAPATNAM ST S,SADANANDA IC A genetic algorithm for unified approach-based predictivemodeling of fatigue crack growth[f1.International JournalofFatigue,2005,27(10·12):1354—1359.\u00A0
】LIJ,MU X D,ZHENG S,et a1.Research on lifetimegrey prediction of electronic equipment based Oilimproved genetic algorithm[C]// International Conference on Advanced Computer Control,January22-24,2009,Singapore.Los Alamitos:IEEE ComputerSociety,2009:493-496.
状态监测
基于振动信号的滚动轴承监测
】m,ANG R Q,ⅪL F,LI X L,et a1.Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods[J]. Mechanical Systems and Signal Processing,2007, 2l(1):193—207.
]GEBRAEEL N,LAWLEYM,LIU R,ct a1.Residual life,predictions from vibration-based degradationsignals: A neural network approach[J].IEEE ,Transactions on Indnstrial Electronics,2004,51(3):694.700.
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