Tools for Analysis of Accelerated Life and Degradation Test Data
|
|
- Tamsyn Terry
- 6 years ago
- Views:
Transcription
1 Acceleraed Sress Tesng and Relably Tools for Analyss of Acceleraed Lfe and Degradaon Tes Daa Presened by: Reuel Smh Unversy of Maryland College Park Sepember-5-6 Sepember , Pensacola Beach, Florda
2 Acceleraed Sress Tesng and Relably Tools and Educaon of Acceleraed Tesng Rsk and relably ools and educaon are becomng more effecve and realsc due o relance upon he Physcs of Falure (PoF) approach PoF modelng uses acceleraed lfe or acceleraed degradaon esng o assess model parameers based on he relaons of sress, damage, and lfe Sandards of Acceleraed Tesng Educaon Concepual defnons and mehodologes Supplemenary example problems Algorhms and codes Sepember , Pensacola Beach, Florda 2
3 Acceleraed Sress Tesng and Relably Overvew of ALT/ADT Tools and Educaon Par I Probablsc physcs of falure (PPoF) Mechansc models of falure mechansms Sress-srengh Damage endurance Performance-requremen Model examples Fague Wear Corroson Creep Sepember , Pensacola Beach, Florda 3
4 Acceleraed Sress Tesng and Relably Overvew of ALT/ADT Tools and Educaon Par II Lfe-Sress Models Acceleraed Lfe Tesng (ALT) [2,3] Analyss: Plong Mehod MLE Bayesan Esmaon Acceleraed Degradaon Tesng (ADT) Sep-Sress and Sress Varyng Tes Types of ALT and Pfalls Tes plannng, Performance, Daa Sorng, and Evaluaon Sepember , Pensacola Beach, Florda 4
5 Acceleraed Sress Tesng and Relably Overvew of ALT/ADT Tools and Educaon Par III Applcaons of Physcs-Based Models for: Relably Engneerng Prognoss Healh Managemen (PHM) Comprehensve Case sudes Feld Examples Sepember , Pensacola Beach, Florda 5
6 Acceleraed Sress Tesng and Relably MLE ALT Example [] Consder he followng mes-o-falure a hree dfferen sress levels Sress (Temperaure) 406 K 436 K 466 K Recorded Falure Tmes (Hours) Sepember , Pensacola Beach, Florda 6
7 Acceleraed Sress Tesng and Relably MLE ALT Example (Con.) If he daa s bes represened by a Webull dsrbuon and he Lfe (L)-Sress (T) model s bes modeled as an Arrhenus model, L = Aexp A Consan o be deermned E a Acvaon energy (ev) T emperaure n Kelvn K Bolzmann consan (/K=605 ev) Esmae produc lfe a gven use-lfe 353 K usng he plong mehod and he MLE mehod and compare Ea KT Sepember , Pensacola Beach, Florda 7
8 Acceleraed Sress Tesng and Relably Plong Mehod MLE ALT Example (Con.) STEP : Plo he lfe dsrbuon a each sress level STEP 2: Fnd he Webull parameers of he dsrbuon α=899, 406 K α=342, 436 K α=88, 466 K Sepember , Pensacola Beach, Florda 8
9 Acceleraed Sress Tesng and Relably MLE ALT Example (Con.) Plong Mehod (Con.) STEP 3: Plo he lfesress a 63.2% falure and solve for Arrhenus parameers Ea 63.2% L63.2% = A63.2% exp KT L = exp 4959 T ˆ β = ( ) / 3 = 2.5 Aˆ ; ˆ / 4959 ; ˆ 63.2% = Ea K = K Ea = 0.43( ev ) 63.2% 63.2% L use = 5, 404hours Sepember , Pensacola Beach, Florda 9
10 Acceleraed Sress Tesng and Relably MLE Mehod MLE ALT Example (Con.) STEP : Oban he Webull-Arrhenus lkelhood expresson Λ = STEP 2: Se he frs dervaves of he lkelhood w.r.. he parameers o zero and solve smulaneously ˆ β = 2.7; Aˆ = ; Eˆ / K = 520 K; Eˆ M = a a = N β ln Ea Aexp KT 0.45( ev ) Ea Aexp KT β exp Λ Λ Λ = 0 ; = 0; = 0 β A E a Ea Aexp KT β 520 L = exp T L use =, 272hours Sepember , Pensacola Beach, Florda 0
11 Acceleraed Sress Tesng and Relably Bayesan ADT Example [, 4] Consder he followng ADT where 25 LED uns each are esed a emperaures 25 C, 65 C, and 05 C [4] Sepember , Pensacola Beach, Florda
12 Acceleraed Sress Tesng and Relably LED degradaon s modeled, Bayesan ADT Example (Con.) Y jk = + β jk exp β 3605 T u T β 2 + ε jk jk he k h me of he j h LED a he h emperaure level ε jk normal dsrbuon NOR(0, σ ε ) If pror knowledge of he parameers β, β 2, β 3, and σ ε s lmed o a unform dsrbuon UNIF(0,00) for each, and an Arrhenus relaonshp s assumed for he ADT, fnd he poseror dsrbuon and mean-me-o-falure for he LED a use emperaure (T u ) 20 C. NOTE: Falure occurs a 50% lumnosy Sepember , Pensacola Beach, Florda 2
13 Acceleraed Sress Tesng and Relably Bayes Rule Mahemacs π Bayesan ADT Example (Con.) The model parameers vecor, θ ( ) ( DATA θ ) ( ) π 0 θ pror dsrbuon of parameers l lkelhood l ( ) ( DATA θ ) π 0( θ ) θ DATA = l( DATA θ ) π ( θ ) d π DATA poseror dsrbuon of parameers θ 0 θ [ θ θ ] 2 θ n Sepember , Pensacola Beach, Florda 3
14 Acceleraed Sress Tesng and Relably Bayesan ADT Example (Con.) The poseror dsrbuon s usually esmaed usng advanced numercal smulaons. For example by usng MCMC (Markov Chan Mone Carlo) smulaon made n he MATLAB sofware we ge: Poseror Parameer Lower Bound Mean Upper Bound β 9.7 x Β β σ ε Sepember , Pensacola Beach, Florda 4
15 Acceleraed Sress Tesng and Relably Bayesan ADT Example (Con.) The MTTF esmae s aken from he Bayesan poseror of each un Recall falure occurs a 50% lumnosy MTTF use =2, 975hours Sepember , Pensacola Beach, Florda 5
16 Acceleraed Sress Tesng and Relably Sep-Sress Example [, 5] Consder a sep-sress es of cable nsulaon ype : Faled + Censored Noe: Sress s defned as volage/cable hckness where nomnal sress s 400 V/mm Sepember , Pensacola Beach, Florda 6
17 Acceleraed Sress Tesng and Relably Sep-Sress Example (Con.) If he pror daa s obaned from hs es of cable nsulaon ype 2, Volage (klovols) Volage (klovols) Tme (mnues) Tme (mnues) Volage (klovols) Volage (klovols) Tme (mnues) Tme (mnues) Updae he model parameers usng Bayesan esmaon Sepember , Pensacola Beach, Florda 7
18 Acceleraed Sress Tesng and Relably Sep-Sress Example (Con.) Ths me he lfe-sress model s represened by he nverse power law (IPL) L = AV p A and p are consans o be deermned V s he sress n vols/mm Sepember , Pensacola Beach, Florda 8
19 Acceleraed Sress Tesng and Relably Sep-Sress Example (Con.) Sepember , Pensacola Beach, Florda 9
20 Acceleraed Sress Tesng and Relably Sep-Sress Example (Con.) The lkelhood for an n-sep-sress model s, number of falure daa pons a sep me of j-h falure daa pon a sep number of rgh censored daa pons a sep me of k-h rgh censored daa pon a sep, and Sepember , Pensacola Beach, Florda 20 ( ) ( ) [ ] = = = = k k j j r k r r r n c j c c c F f l * * * * * *,,,,,, 2 2 ( ) ( ) [ ] ( ) ( ) [ ] ( ) [ ] ( ) n p c p c p c p c c V V AV AV AV V f AV V F j j j j j + = + + = + = 2 2 * * * * * exp, exp, τ τ τ τ β τ β β β c j * cj r k * ck
21 Acceleraed Sress Tesng and Relably Sep-Sress Example (Con.) 000 Prors 000 Poserors freq freq freq A Sep-sress es daa freq A p Bayesan Updang 000 p freq freq Parameer Lower Bound Upper Bound A 4.38 x x 0-5 p.37 x β.74 x Parameer Lower Bound Upper Bound A 3.70 x x 0-5 p.2 x β 4.68 x Sepember , Pensacola Beach, Florda 2
22 Acceleraed Sress Tesng and Relably Closng Remarks Relably engneers should be cognzan and aware of he mporance of acceleraed esng pracces Necessary componens for undersandng acceleraed esng Theory Mehods and models Tools and applcaons Sepember , Pensacola Beach, Florda 22
23 Acceleraed Sress Tesng and Relably Abou Reuel Smh Reuel Smh s a PhD level Relably Engneerng graduae suden a he A.J. Clark School of Engneerng a he Unversy of Maryland College Park. Hs curren research s n he area of fague crack propagaon, deecon, and modelng. Reuel Smh receved hs M.S. degree n boh Aerospace Engneerng and Mechancal Engneerng from he Unversy of Maryland College Park. Sepember , Pensacola Beach, Florda 23
24 Acceleraed Sress Tesng and Relably Abou Dr. Mohammad Modarres Mohammad Modarres s he Ncole Y. Km Emnen Professor of Engneerng and Drecor Cener for Rsk and Relably, A.J. Clark School of Engneerng, Unversy of Maryland, College Park. Hs research areas are probablsc rsk assessmen and managemen, uncerany analyss and physcs of falure degradaon modelng. Professor Modarres has over 350 papers n archval journals and proceedngs of conferences, ncludng several books n varous areas of rsk and relably engneerng. He s a Unversy of Maryland Dsngushed Scholar. Professor Modarres receved hs M.S. and PhD n Nuclear Engneerng from MIT and M.S. n Mechancal Engneerng also from MIT. Sepember , Pensacola Beach, Florda 24
25 Acceleraed Sress Tesng and Relably References. M. Modarres, M. Amr and C. Jackson, Probablsc Physcs of Falure Approach o Relably: Modelng, Acceleraed Tesng, Prognoss and Relably Assessmen, College Park, MD: Cener for Rsk and Relably A.J. Clark School of Engneerng, 205. (PDF verson s avalable for download from hp://crr.umd.edu/node/56) 2. W. Nelson, Acceleraed Tesng - Sascal Models, Tes Plans, and Daa Analyss, Hoboken, NJ: John Wley and Sons, W. Nelson, Acceleraed Tesng - Sascal Models, Tes Plans, and Daa Analyss, New York: John Wley and Sons, M. S. Hamada, A. Wlson, S. Reese and H. Marz, "Chaper 8: Usng Degradaon Daa o Assess Relably," n Bayesan Relably, New York, Sprnger Scence+Busness Meda, 2008, pp W. Nelson, "Acceleraed Lfe Tesng Sep-Sress Models and Daa Analyses," IEEE Transacons of Relably, Vols. R-29, no. 2, pp , Sepember , Pensacola Beach, Florda 25
26 Acceleraed Sress Tesng and Relably Conac Informaon: Quesons? Mohammad Modarres Professor of Relably Engneerng Tel: Fax: Sepember , Pensacola Beach, Florda 26
27 Acceleraed Sress Tesng and Relably Thank You THE END! Sepember , Pensacola Beach, Florda 27
5th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2015)
5h Inernaonal onference on Advanced Desgn and Manufacurng Engneerng (IADME 5 The Falure Rae Expermenal Sudy of Specal N Machne Tool hunshan He, a, *, La Pan,b and Bng Hu 3,c,,3 ollege of Mechancal and
More informationCHAPTER 10: LINEAR DISCRIMINATION
CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g
More informationJ i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.
umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal
More informationBayesian Inference of the GARCH model with Rational Errors
0 Inernaonal Conference on Economcs, Busness and Markeng Managemen IPEDR vol.9 (0) (0) IACSIT Press, Sngapore Bayesan Inference of he GARCH model wh Raonal Errors Tesuya Takash + and Tng Tng Chen Hroshma
More informationFall 2010 Graduate Course on Dynamic Learning
Fall 200 Graduae Course on Dynamc Learnng Chaper 4: Parcle Flers Sepember 27, 200 Byoung-Tak Zhang School of Compuer Scence and Engneerng & Cognve Scence and Bran Scence Programs Seoul aonal Unversy hp://b.snu.ac.kr/~bzhang/
More informationComparison of Weibayes and Markov Chain Monte Carlo methods for the reliability analysis of turbine nozzle components with right censored data only
Comparson of Webayes and Markov Chan Mone Carlo mehods for he relably analyss of urbne nozzle componens wh rgh censored daa only Francesco Cannarle,2, Mchele Compare,2, Sara Maafrr 3, Fauso Carlevaro 3,
More informationRobustness Experiments with Two Variance Components
Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference
More informationHEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD
Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,
More informationWiH Wei He
Sysem Idenfcaon of onlnear Sae-Space Space Baery odels WH We He wehe@calce.umd.edu Advsor: Dr. Chaochao Chen Deparmen of echancal Engneerng Unversy of aryland, College Par 1 Unversy of aryland Bacground
More informationOutline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model
Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon
More informationMixture Model. A dissertation presented to. the faculty of. In partial fulfillment. of the requirements for the degree. Doctor of Philosophy.
Sem-paramerc Bayesan Inference of Acceleraed Lfe Tes Usng Drchle Process Mure Model A dsseraon presened o he faculy of he Russ College of Engneerng and Technology of Oho Unversy In paral fulfllmen of he
More informationOperational Risk Modeling and Quantification
herry Bud Workshop 5, Keo Unversy Operaonal Rsk Modelng and Quanfcaon Pavel V. Shevchenko SIRO Mahemacal and Informaon Scences, Sydney, Ausrala E-mal: Pavel.Shevchenko@csro.au Agenda Loss Dsrbuon Approach
More informationOrdinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s
Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class
More informationCH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC
CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More informationJohn Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany
Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy
More informationFiltrage particulaire et suivi multi-pistes Carine Hue Jean-Pierre Le Cadre and Patrick Pérez
Chaînes de Markov cachées e flrage parculare 2-22 anver 2002 Flrage parculare e suv mul-pses Carne Hue Jean-Perre Le Cadre and Parck Pérez Conex Applcaons: Sgnal processng: arge rackng bearngs-onl rackng
More informationFI 3103 Quantum Physics
/9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon
More informationTime-interval analysis of β decay. V. Horvat and J. C. Hardy
Tme-nerval analyss of β decay V. Horva and J. C. Hardy Work on he even analyss of β decay [1] connued and resuled n he developmen of a novel mehod of bea-decay me-nerval analyss ha produces hghly accurae
More informationModélisation de la détérioration basée sur les données de surveillance conditionnelle et estimation de la durée de vie résiduelle
Modélsaon de la dééroraon basée sur les données de survellance condonnelle e esmaon de la durée de ve résduelle T. T. Le, C. Bérenguer, F. Chaelan Unv. Grenoble Alpes, GIPSA-lab, F-38000 Grenoble, France
More informationMulti-Sensor Degradation Data Analysis
A publcaon of CHEMICAL ENGINEERING TRANSACTIONS VOL. 33 23 Gues Edors: Enrco Zo Pero Barald Copyrgh 23 AIDIC Servz S.r.l. ISBN 978-88-9568-24-2; ISSN 974-979 The Ialan Assocaon of Chemcal Engneerng Onlne
More informationNew M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)
Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor
More informationPerformance Analysis for a Network having Standby Redundant Unit with Waiting in Repair
TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen
More informationModern Time-Rate Relations
Modern Tme-Rae Relaons Slde 1 Orenaon Tme-Rae Relaons: New me-rae relaons whch ulze he followng componens: Hyperbolc and modfed-hyperbolc relaons, Power-law/sreched exponenal relaons, and Exponenal relaons
More information( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model
BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng
More informationJanuary Examinations 2012
Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons
More informationLecture 9: Dynamic Properties
Shor Course on Molecular Dynamcs Smulaon Lecure 9: Dynamc Properes Professor A. Marn Purdue Unversy Hgh Level Course Oulne 1. MD Bascs. Poenal Energy Funcons 3. Inegraon Algorhms 4. Temperaure Conrol 5.
More informationRELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA
RELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA Mchaela Chocholaá Unversy of Economcs Braslava, Slovaka Inroducon (1) one of he characersc feaures of sock reurns
More informationTHE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS
THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he
More informationIn the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!
ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal
More informationABSTRACT KEYWORDS. Bonus-malus systems, frequency component, severity component. 1. INTRODUCTION
EERAIED BU-MAU YTEM ITH A FREQUECY AD A EVERITY CMET A IDIVIDUA BAI I AUTMBIE IURACE* BY RAHIM MAHMUDVAD AD HEI HAAI ABTRACT Frangos and Vronos (2001) proposed an opmal bonus-malus sysems wh a frequency
More informationReactive Methods to Solve the Berth AllocationProblem with Stochastic Arrival and Handling Times
Reacve Mehods o Solve he Berh AllocaonProblem wh Sochasc Arrval and Handlng Tmes Nsh Umang* Mchel Berlare* * TRANSP-OR, Ecole Polyechnque Fédérale de Lausanne Frs Workshop on Large Scale Opmzaon November
More informationDepartment of Economics University of Toronto
Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of
More informationHandout # 6 (MEEN 617) Numerical Integration to Find Time Response of SDOF mechanical system Y X (2) and write EOM (1) as two first-order Eqs.
Handou # 6 (MEEN 67) Numercal Inegraon o Fnd Tme Response of SDOF mechancal sysem Sae Space Mehod The EOM for a lnear sysem s M X DX K X F() () X X X X V wh nal condons, a 0 0 ; 0 Defne he followng varables,
More informationM. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria
IOSR Journal of Mahemacs (IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume 0, Issue 4 Ver. IV (Jul-Aug. 04, PP 40-44 Mulple SolonSoluons for a (+-dmensonalhroa-sasuma shallow waer wave equaon UsngPanlevé-Bӓclund
More informationSingle-loop System Reliability-Based Design & Topology Optimization (SRBDO/SRBTO): A Matrix-based System Reliability (MSR) Method
10 h US Naonal Congress on Compuaonal Mechancs Columbus, Oho 16-19, 2009 Sngle-loop Sysem Relably-Based Desgn & Topology Opmzaon (SRBDO/SRBTO): A Marx-based Sysem Relably (MSR) Mehod Tam Nguyen, Junho
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationSolution in semi infinite diffusion couples (error function analysis)
Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of
More informationBayesian Estimation of the Kumaraswamy Inverse Weibull Distribution
Journal of Sascal Theory and Applcaons, Vol 16, No 2 June 217) 248 26 Bayesan Esmaon of he Kumaraswamy Inverse Webull Dsrbuon Felpe R S de Gusmão Deparmen of Sascs, Federal Unversy of São Carlos, Brazl
More informationReliability growth via testing Lawrence M. Leemis a a
Ths arcle was downloaded by: [College of Wllam & Mary] n: 9 March 200 Access deals: Access Deals: [subscrpon number 978] Publsher Taylor & Francs Informa Ld Regsered n England and Wales Regsered Number:
More informationStochastic Programming handling CVAR in objective and constraint
Sochasc Programmng handlng CVAR n obecve and consran Leondas Sakalaskas VU Inse of Mahemacs and Informacs Lhana ICSP XIII Jly 8-2 23 Bergamo Ialy Olne Inrodcon Lagrangan & KKT condons Mone-Carlo samplng
More informationIntroduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms
Course organzaon Inroducon Wee -2) Course nroducon A bref nroducon o molecular bology A bref nroducon o sequence comparson Par I: Algorhms for Sequence Analyss Wee 3-8) Chaper -3, Models and heores» Probably
More informationSurvival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System
Communcaons n Sascs Theory and Mehods, 34: 475 484, 2005 Copyrgh Taylor & Francs, Inc. ISSN: 0361-0926 prn/1532-415x onlne DOI: 10.1081/STA-200047430 Survval Analyss and Relably A Noe on he Mean Resdual
More informationLecture Slides for INTRODUCTION TO. Machine Learning. ETHEM ALPAYDIN The MIT Press,
Lecure Sldes for INTRDUCTIN T Machne Learnng ETHEM ALAYDIN The MIT ress, 2004 alpaydn@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/2ml CHATER 3: Hdden Marov Models Inroducon Modelng dependences n npu; no
More informationIncreasing the Probablility of Timely and Correct Message Delivery in Road Side Unit Based Vehicular Communcation
Halmsad Unversy For he Developmen of Organsaons Producs and Qualy of Lfe. Increasng he Probablly of Tmely and Correc Message Delvery n Road Sde Un Based Vehcular Communcaon Magnus Jonsson Krsna Kuner and
More informationOn computing differential transform of nonlinear non-autonomous functions and its applications
On compung dfferenal ransform of nonlnear non-auonomous funcons and s applcaons Essam. R. El-Zahar, and Abdelhalm Ebad Deparmen of Mahemacs, Faculy of Scences and Humanes, Prnce Saam Bn Abdulazz Unversy,
More informationTrack Properities of Normal Chain
In. J. Conemp. Mah. Scences, Vol. 8, 213, no. 4, 163-171 HIKARI Ld, www.m-har.com rac Propes of Normal Chan L Chen School of Mahemacs and Sascs, Zhengzhou Normal Unversy Zhengzhou Cy, Hennan Provnce, 4544,
More informationComparison of Supervised & Unsupervised Learning in βs Estimation between Stocks and the S&P500
Comparson of Supervsed & Unsupervsed Learnng n βs Esmaon beween Socks and he S&P500 J. We, Y. Hassd, J. Edery, A. Becker, Sanford Unversy T I. INTRODUCTION HE goal of our proec s o analyze he relaonshps
More informationApproximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy
Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae
More informationESTIMATIONS OF RESIDUAL LIFETIME OF ALTERNATING PROCESS. COMMON APPROACH TO ESTIMATIONS OF RESIDUAL LIFETIME
Srucural relably. The heory and pracce Chumakov I.A., Chepurko V.A., Anonov A.V. ESTIMATIONS OF RESIDUAL LIFETIME OF ALTERNATING PROCESS. COMMON APPROACH TO ESTIMATIONS OF RESIDUAL LIFETIME The paper descrbes
More informationGear System Time-varying Reliability Analysis Based on Elastomer Dynamics
A publcaon of CHEMICAL ENGINEERING TRANSACTIONS VOL. 33, 013 Gues Edors: Enrco Zo, Pero Barald Copyrgh 013, AIDIC Servz S.r.l., ISBN 978-88-95608-4-; ISSN 1974-9791 The Ialan Assocaon of Chemcal Engneerng
More informationReal time processing with low cost uncooled plane array IR camera-application to flash nondestructive
hp://dx.do.org/0.6/qr.000.04 Real me processng wh low cos uncooled plane array IR camera-applcaon o flash nondesrucve evaluaon By Davd MOURAND, Jean-Chrsophe BATSALE L.E.P.T.-ENSAM, UMR 8508 CNRS, Esplanade
More informationAn introduction to Support Vector Machine
An nroducon o Suppor Vecor Machne 報告者 : 黃立德 References: Smon Haykn, "Neural Neworks: a comprehensve foundaon, second edon, 999, Chaper 2,6 Nello Chrsann, John Shawe-Tayer, An Inroducon o Suppor Vecor Machnes,
More informationP R = P 0. The system is shown on the next figure:
TPG460 Reservor Smulaon 08 page of INTRODUCTION TO RESERVOIR SIMULATION Analycal and numercal soluons of smple one-dmensonal, one-phase flow equaons As an nroducon o reservor smulaon, we wll revew he smples
More informationALLOCATING TOLERANCES FOR VEE-GROOVE FIBER ALIGNMENT
ALLOCATING TOLERANCES FOR VEE-GROOVE FIBER ALIGNMENT Maheu Barraa and R. Ryan Vallance Precson Sysems Laboraory Unversy of Kenucky Lengon KY * S. Kan J. Lehman and Burke Hunsaker Teradyne Connecon Sysems
More informationClustering (Bishop ch 9)
Cluserng (Bshop ch 9) Reference: Daa Mnng by Margare Dunham (a slde source) 1 Cluserng Cluserng s unsupervsed learnng, here are no class labels Wan o fnd groups of smlar nsances Ofen use a dsance measure
More informationStochastic Repair and Replacement with a single repair channel
Sochasc Repar and Replacemen wh a sngle repar channel MOHAMMED A. HAJEEH Techno-Economcs Dvson Kuwa Insue for Scenfc Research P.O. Box 4885; Safa-309, KUWAIT mhajeeh@s.edu.w hp://www.sr.edu.w Absrac: Sysems
More informationRelative controllability of nonlinear systems with delays in control
Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.
More informationMachine Learning Linear Regression
Machne Learnng Lnear Regresson Lesson 3 Lnear Regresson Bascs of Regresson Leas Squares esmaon Polynomal Regresson Bass funcons Regresson model Regularzed Regresson Sascal Regresson Mamum Lkelhood (ML)
More informationProbabilistic Forecasting of Wind Power Ramps Using Autoregressive Logit Models
obablsc Forecasng of Wnd Poer Ramps Usng Auoregressve Log Models James W. Taylor Saїd Busness School, Unversy of Oford 8 May 5 Brunel Unversy Conens Wnd poer and ramps Condonal AR log (CARL) Condonal AR
More informationIterative Learning Control and Applications in Rehabilitation
Ierave Learnng Conrol and Applcaons n Rehablaon Yng Tan The Deparmen of Elecrcal and Elecronc Engneerng School of Engneerng The Unversy of Melbourne Oulne 1. A bref nroducon of he Unversy of Melbourne
More informationPartial Availability and RGBI Methods to Improve System Performance in Different Interval of Time: The Drill Facility System Case Study
Open Journal of Modellng and Smulaon, 204, 2, 44-53 Publshed Onlne Ocober 204 n ScRes. hp://www.scrp.org/journal/ojms hp://dx.do.org/0.4236/ojms.204.2406 Paral Avalably and RGBI Mehods o Improve Sysem
More informationSOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β
SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose
More informationMotion in Two Dimensions
Phys 1 Chaper 4 Moon n Two Dmensons adzyubenko@csub.edu hp://www.csub.edu/~adzyubenko 005, 014 A. Dzyubenko 004 Brooks/Cole 1 Dsplacemen as a Vecor The poson of an objec s descrbed by s poson ecor, r The
More informationThe Performance of Optimum Response Surface Methodology Based on MM-Estimator
The Performance of Opmum Response Surface Mehodology Based on MM-Esmaor Habshah Md, Mohd Shafe Musafa, Anwar Frano Absrac The Ordnary Leas Squares (OLS) mehod s ofen used o esmae he parameers of a second-order
More informationUNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION
INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he
More informationSampling Procedure of the Sum of two Binary Markov Process Realizations
Samplng Procedure of he Sum of wo Bnary Markov Process Realzaons YURY GORITSKIY Dep. of Mahemacal Modelng of Moscow Power Insue (Techncal Unversy), Moscow, RUSSIA, E-mal: gorsky@yandex.ru VLADIMIR KAZAKOV
More information( ) [ ] MAP Decision Rule
Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure
More informationHidden Markov Models Following a lecture by Andrew W. Moore Carnegie Mellon University
Hdden Markov Models Followng a lecure by Andrew W. Moore Carnege Mellon Unversy www.cs.cmu.edu/~awm/uorals A Markov Sysem Has N saes, called s, s 2.. s N s 2 There are dscree meseps, 0,, s s 3 N 3 0 Hdden
More informationIncluding the ordinary differential of distance with time as velocity makes a system of ordinary differential equations.
Soluons o Ordnary Derenal Equaons An ordnary derenal equaon has only one ndependen varable. A sysem o ordnary derenal equaons consss o several derenal equaons each wh he same ndependen varable. An eample
More informationA NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION
S19 A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION by Xaojun YANG a,b, Yugu YANG a*, Carlo CATTANI c, and Mngzheng ZHU b a Sae Key Laboraory for Geomechancs and Deep Underground Engneerng, Chna Unversy
More informationFTCS Solution to the Heat Equation
FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence
More informationVariants of Pegasos. December 11, 2009
Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on
More informationPhD/MA Econometrics Examination. January, 2019
Economercs Comprehensve Exam January 2019 Toal Tme: 8 hours MA sudens are requred o answer from A and B. PhD/MA Economercs Examnaon January, 2019 PhD sudens are requred o answer from A, B, and C. The answers
More informationMethodological Aspects of Assessment and Optimization of the Reliability of Electrical Installations of the Railway Self-Propelled Rolling Stock
Mehodologcal Aspecs of Assessmen and Opmzaon of he Relably of Elecrcal Insallaons of he Ralway Self-Propelled Rollng Sock Mukhamedova Zyoda Gafurdanovna, Yakubov Mrall Sagaovch Tashken nsue of ralway engneerng
More informationBaseflow Analysis. Objectives. Baseflow definition and significance. Reservoir model for recession analysis. Physically-based aquifer model
Objecves Baseflow Analss. Undersand he concepual bass of baseflow analss.. Esmae waershed-average hdraulc parameers and groundwaer recharge raes. dscharge (m s - ) 0.6 0.4 0. baseflow dscharge (m s - )
More informationDynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005
Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc
More information[Link to MIT-Lab 6P.1 goes here.] After completing the lab, fill in the following blanks: Numerical. Simulation s Calculations
Chaper 6: Ordnary Leas Squares Esmaon Procedure he Properes Chaper 6 Oulne Cln s Assgnmen: Assess he Effec of Sudyng on Quz Scores Revew o Regresson Model o Ordnary Leas Squares () Esmaon Procedure o he
More informationCubic Bezier Homotopy Function for Solving Exponential Equations
Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.
More informationImplementation of Quantized State Systems in MATLAB/Simulink
SNE T ECHNICAL N OTE Implemenaon of Quanzed Sae Sysems n MATLAB/Smulnk Parck Grabher, Mahas Rößler 2*, Bernhard Henzl 3 Ins. of Analyss and Scenfc Compung, Venna Unversy of Technology, Wedner Haupsraße
More informationBayes rule for a classification problem INF Discriminant functions for the normal density. Euclidean distance. Mahalanobis distance
INF 43 3.. Repeon Anne Solberg (anne@f.uo.no Bayes rule for a classfcaon problem Suppose we have J, =,...J classes. s he class label for a pxel, and x s he observed feaure vecor. We can use Bayes rule
More informationThe JCSS Probabilistic Model Code for Timber Examples and Discussion
The JCSS Probablsc Model Code for Tmber Examples and Dscusson Jochen Köhler Research Assocae Mchael Faber Professor Insue of Srucural Engneerng Swss Federal Insue of Technology Zurch, Swzerland 1. Inroducon
More informationAnisotropic Behaviors and Its Application on Sheet Metal Stamping Processes
Ansoropc Behavors and Is Applcaon on Shee Meal Sampng Processes Welong Hu ETA-Engneerng Technology Assocaes, Inc. 33 E. Maple oad, Sue 00 Troy, MI 48083 USA 48-79-300 whu@ea.com Jeanne He ETA-Engneerng
More informationDiscrete Markov Process. Introduction. Example: Balls and Urns. Stochastic Automaton. INTRODUCTION TO Machine Learning 3rd Edition
EHEM ALPAYDI he MI Press, 04 Lecure Sldes for IRODUCIO O Machne Learnng 3rd Edon alpaydn@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/ml3e Sldes from exboo resource page. Slghly eded and wh addonal examples
More informationComputing Relevance, Similarity: The Vector Space Model
Compung Relevance, Smlary: The Vecor Space Model Based on Larson and Hears s sldes a UC-Bereley hp://.sms.bereley.edu/courses/s0/f00/ aabase Managemen Sysems, R. Ramarshnan ocumen Vecors v ocumens are
More informationAdditive Outliers (AO) and Innovative Outliers (IO) in GARCH (1, 1) Processes
Addve Oulers (AO) and Innovave Oulers (IO) n GARCH (, ) Processes MOHAMMAD SAID ZAINOL, SITI MERIAM ZAHARI, KAMARULZAMMAN IBRAHIM AZAMI ZAHARIM, K. SOPIAN Cener of Sudes for Decson Scences, FSKM, Unvers
More informationEVALUATION OF FORCE COEFFICIENTS FOR A 2-D ANGLE SECTION USING REALIZABLE k-ε TURBULENCE MODEL
The Sevenh Asa-Pacfc Conference on Wnd Engneerng, November 8-, 009, Tape, Tawan EVALUATION OF FORCE COEFFICIENTS FOR A -D ANGLE SECTION USING REALIZABLE k-ε TURBULENCE MODEL S. Chra Ganapah, P. Harkrshna,
More informationSimulation and Probability Distribution
CHAPTER Probablty, Statstcs, and Relablty for Engneers and Scentsts Second Edton PROBABILIT DISTRIBUTION FOR CONTINUOUS RANDOM VARIABLES A. J. Clark School of Engneerng Department of Cvl and Envronmental
More informationScattering at an Interface: Oblique Incidence
Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may
More informationComparison of Differences between Power Means 1
In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,
More informationAdvanced time-series analysis (University of Lund, Economic History Department)
Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng
More informationStructural Damage Detection Using Optimal Sensor Placement Technique from Measured Acceleration during Earthquake
Cover page Tle: Auhors: Srucural Damage Deecon Usng Opmal Sensor Placemen Technque from Measured Acceleraon durng Earhquake Graduae Suden Seung-Keun Park (Presener) School of Cvl, Urban & Geosysem Engneerng
More informationLinear Response Theory: The connection between QFT and experiments
Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure
More informationParameter Estimation of Three-Phase Induction Motor by Using Genetic Algorithm
360 Journal of Elecrcal Engneerng & Technology Vol. 4, o. 3, pp. 360~364, 009 Parameer Esmaon of Three-Phase Inducon Moor by Usng Genec Algorhm Seesa Jangj and Panhep Laohacha* Absrac Ths paper suggess
More informationStat 543 Exam 2 Spring 2016
Stat 543 Exam 2 Sprng 206 I have nether gven nor receved unauthorzed assstance on ths exam. Name Sgned Date Name Prnted Ths Exam conssts of questons. Do at least 0 of the parts of the man exam. I wll score
More informationIntegration of Reliability- And Possibility-Based Design Optimizations Using Performance Measure Approach
SAE Keynoe Paper: Cho, K.K., Youn, B.D., and Du, L., Inegraon of Relably- and Possbly-Based Desgn Opmzaons Usng Performance Measure Approach, 005 SAE World Congress, Aprl 11-14, 005, Dero, MI. 005-01-034
More information( ) () we define the interaction representation by the unitary transformation () = ()
Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger
More information12d Model. Civil and Surveying Software. Drainage Analysis Module Detention/Retention Basins. Owen Thornton BE (Mech), 12d Model Programmer
d Model Cvl and Surveyng Soware Dranage Analyss Module Deenon/Reenon Basns Owen Thornon BE (Mech), d Model Programmer owen.hornon@d.com 4 January 007 Revsed: 04 Aprl 007 9 February 008 (8Cp) Ths documen
More informationComprehensive Integrated Simulation and Optimization of LPP for EUV Lithography Devices
Comprehense Inegraed Smulaon and Opmaon of LPP for EUV Lhograph Deces A. Hassanen V. Su V. Moroo T. Su B. Rce (Inel) Fourh Inernaonal EUVL Smposum San Dego CA Noember 7-9 2005 Argonne Naonal Laboraor Offce
More informationOn One Analytic Method of. Constructing Program Controls
Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna
More information