Chapter 5 Reliability of Systems

Size: px
Start display at page:

Download "Chapter 5 Reliability of Systems"

Transcription

1 Chapter 5 Reliability of Systems Hey! Can you tell us how to analyze complex systems? Serial Configuration Parallel Configuration Combined Series-Parallel C. Ebeling, Intro to Reliability & Maintainability Engineering, Chapter 5 2 nd ed. Waveland Press, Inc. Copyright

2 Serial Configuration Reliability Block Diagram E 1 = the event, component 1 does not fail, and E 2 = the event, component 2 does not fail, then P{E 1 } = R 1 and P{E 2 } = R 2 where R 1 = the reliability of component 1, and R 2 = the reliability of component 2. Therefore assuming independence: R s = P{E 1 E 2 } = P{E 1 } P{E 2 } = R 1 R 2 Chapter 5 2

3 Multiple Components in Series Generalizing to n mutually independent components in series; R s (t) = R 1 (t) x R 2 (t) x... x R n (t) and R s (t) min {R 1 (t), R 2 (t),..., R n (t)} Chapter 5 3

4 Component Count vs. System Reliability Component Number of Components Reliability x x x x System Reliability Chapter 5 4

5 Constant Failure Rate Components n λi t n n - t - t R s(t) = R() t = i e = e i=1 = λi λs e i=1 i=1 where λ s = λ i n i=1 Chapter 5 5

6 Weibull Components R s(t) = n e F H G i=1 n F H t Iβi - = e i=1 θi K J t θi I K βi λ (t) = e - n β t I i HG θ i K J i=1 F e - L NM n i=1 β θ n β t I i HG θ i K J i=1 F i i F HG t θ i I K J β i -1 O QP = n F i t i H G β I -1 i i K J θ θ i=1 β Chapter 5 6

7 Components in Series - Example A communications satellite consists of the following components: Probability Shape Characteristic Component Distribution Parameter life Power unit Weibull ,800 hr. Receiver Weibull ,000 hr. Transmitter Weibull ,000 hr. Antennae Exponential MTTF = 100,000 hr. Chapter 5 7

8 λ S () t Components in Series - Example t 14. t 18. t , 43800, 75000, 75000, 68000, 68000, , = F H G I K J + F H G I K J + F H G I K J + R () t e e e e s t t t t 43, , , , 000 = F H G I K J F H G I K J F H G I K J 17, , , 520 F H G I K J F H G I K J F H G I K J 17, 520 R (, ) 43, , , , 000 s = e e e e =. 62 Chapter 5 8

9 Class Exercise The failure distribution of the main landing gear of a commercial airliner is Weibull with a shape parameter of 1.6 and a characteristic life of 10,000 landings. The nose gear also has a Weibull distribution with a shape parameter of.90 and a characteristic life of landings. What is the reliability of the landing gear system if the system is to be overhauled after 1000 landings? Chapter 5 9

10 Class Exercise - solution , 15, R( 1000) e e (. 975) (. 916). 893 = F H G I K J F H G I K J = = Animated Chapter 5 10

11 Parallel Configuration R s =P{E1 E2} = 1 - P{(E1 E2) c } R 1 R 2 =1-P{E1 c E2 c } =1-P{E1 c }P{E2 c } = 1 - (1-R 1 ) (1-R 2 ) R n where E1 = event, component 1 does not fail E2 = event, component 2 does not fail, and Pr{E1} = R 1 and Pr{E2} = R 2 The probability at least one component does not fail! Chapter 5 11

12 Parallel Configuration - Generalization R s n i= 1 [ R ] i (t) = 1-1- (t) R s (t) >= max {R 1 (t), R 2 (t),, R n (t)} Animated Chapter 5 12

13 Parallel Configuration - CFR Model R s n (t) = it e λ i= 1 Chapter 5 13

14 For n = 2: s Parallel Configuration 2-component CFR Model n - λ t s 1 i= 1 1- e i R (t) = - -λ t -λ t R (t) = 1 - (1 - e 1 )(1 - e 2 ) -λ t -λ t -( λ + λ )t = e + e - e z z z z -λ1t -λ 2t -( λ MTTF = R (t)dt = e dt + e dt - 1+ λ 2 )t e dt 0 s = λ 1 λ + 2 λ1 λ2 Chapter 5 14

15 Parallel Configuration - Weibull Model L i n t 1 1 s i= 1 N M F O P H G I Β K J θ Q R (t) = - - e i If n identical Weibull components are in parallel: R (t) = - s L NM t 1 1 F H G I Β K J θ - e O QP n Chapter 5 15

16 Parallel Configuration - Example A circuit breaker has a Weibull failure distribution (against a power surge) with beta equal to.75 and a characteristic life of 12 years F H G I K J. 75 R() 1 = e =. 856 Determine the one year reliability if two identical circuit breakers are redundant. Chapter 5 16

17 Parallel Configuration Example Solution Two redundant breakers have a reliability function of: Rt () L = 1 NM 1 e F t H G I K J 12 O. 75 QP 2 L = NM F 1 H G I K J O. 75 P Q 12 2 R() e P = 1 ( ) = Question: how are two redundant circuit breakers physically configured? Chapter 5 17

18 Combined Series - Parallel Systems R 1 R 2 R 3 R 6 R 4 R 5 Chapter 5 18

19 Combined Series - Parallel Systems A B R 1 R 2 R 3 R 6 C R 4 R 5 Chapter 5 19

20 Combined Series - Parallel Systems A R 1 R 2 R A = [ 1 - (1 - R 1 ) (1 - R 2 )] Chapter 5 20

21 Combined Series - Parallel Systems A R A = [ 1 - (1 - R 1 ) (1 - R 2 )] R 1 R 2 A B R 1 R B = R A R 3 R 3 R 2 Chapter 5 21

22 Combined Series - Parallel Systems C R C = R 4 R 5 R 4 R 5 R B R 6 R C R s = [1 - (1 - R B ) (1 - R c ) ] R 6 Chapter 5 22

23 High Level Redundancy R high = 1 - (1-R 2 ) 2 = 1 - [1-2 R 2 +R 4 ] = 2R 2 -R 4 Animated Chapter 5 23

24 Low Level Redundancy R LOW = [1-(1-R) 2 ] 2 = [1-(1-2R+R 2 )] 2 = (2R-R 2 ) 2 Chapter 5 24

25 High vs Low Level Redundancy R low -R high =(2R-R 2 ) 2 -(2R 2 -R 4 ) =R 2 (2-R) 2 -R 2 (2 - R 2 ) =R 2 [4-4R+R 2-2+R 2 ] =2R 2 [R 2-2R+1]=2R 2 (R - 1) 2 0 Chapter 5 25

26 k-out-of-n Redundancy Let n = the number of redundant, identical and independent components each having a reliability of R. Let X = a random variable, the number of components (out of n components) operating. Then F n Pr{ X = x} = P(x) = H G I K J R x(1- R ) x n-x If k (<= n) components must operate for the system to operate: R = s N x = K P(x) Chapter 5 26

27 k-out-of-n Redundancy Exponential Distribution R (t) = s N N - xt x=k F H G I K J λ 1 -λt x e -e N-x z MTTF = R (t)dt = 1 N 1 0 s x=k λ x Chapter 5 27

28 A Very Good Example Out of the 12 identical AC generators on the C-5 aircraft, at least 9 of them must be operating in order for the aircraft to complete its mission. Failures are known to follow an exponential distribution with a mean of 100 operating hours. What is the reliability of the generator system over a 10 hour mission? Find the MTTF. Chapter 5 28

29 A Very Good Solution Let T i = time to failure of the i th generator 10/100 Pr{ Ti 10} = e =.9048 R x [ ] 12 -x (t) = - =.9782 x 12 s x= 9 M TTF 12 1 = 100 = hours x x = 9 Chapter 5 29

30 Complex Configurations a. linked network: A B E C D Chapter 5 30

31 Decomposition Approach (b) Component E does not fail, R E : A C B D R (b) = [1-(1-R A )(1-R B )][1-(1-R C )(1-R D )] Chapter 5 31

32 Decomposition Approach (c ) Component E fails (1-R E ): A C B D R (c) = 1-(1-R A R C ) (1-R B R D ) Chapter 5 32

33 Decomposition Approach R S = R E R (b) + (1 - R E ) R (c ) if R A = R B =.9, R C = R D =.95, and R E =.80, then R (b) = [1-(1-.9) 2 ] [1-(1-.95) 2 ] =.99 x.9975 =.9875 R (c ) = 1 - [1-(.9) (.95)] 2 = R s =.8 (.9875) + (1-.8) ( ) =.9858 Chapter 5 33

34 Enumeration Method S = success; F = failure A B C D E System Probability S S S S S S F S S S S S S F S S S S S S F S S S S S S F S S S S S S F S F F S S S F S F F S S S S S F F S F S S S F F S TOTAL.9858 Chapter 5 34

35 Example Automotive Braking System WC1 BP1 M C WC2 WC3 WC4 BP2 BP3 BP4 Chapter 5 35

36 WC1 BP1 M C WC2 WC3 BP2 BP3 Case I. BP3 fails and BP4 is operational. 1. P I = [1 - R(BP)] R(BP) 2. R f = R(M){1 - [1 - R(WC) R(BP)] 2 [1 - R(WC)]}. 3. R{mechanical} = R(C) 4. These two subsystems operate in parallel, therefore, R I =1-[1-R f ] [1 - R(C)]. Case II. BP4 fails and BP3 is operational. Then P II = P I and R II = R I (by symmetry of the network and assuming identical reliabilities) WC4 Chapter 5 36

37 WC1 BP1 WC2 BP2 M WC3 BP3 Case III. BP3 and BP4 have failed. C WC4 BP4 1. P III = [1 - R(BP)] 2 2. Cable system has failed. 3. R III = R(M) {1 - [1 - R(WC) R(BP)] 2 } Chapter 5 37

38 WC1 BP1 WC2 BP2 M WC3 C WC4 Case IV. Both BP3 and BP4 are operational. 1. P IV = [R(BP)] 2 2. R f = R(M){1 - [1 - R(WC) R(BP)]2 [1 - R(WC)]2}. 3. R{cable system} = R(C) 4. R IV = 1 - [1 - Rf] [1 - R(C)] The overall system reliability can now be found from: R S =P I R I +P II R II +P III R III +P IV R IV Chapter 5 38

39 A Common Everyday Reliability Block Diagram to Solve R A =.8 R D =.95 R E =.7 R B =.9 R C =.9 R F =.8 Help! Where can I find a common everyday reliability block diagram? Chapter 5 39

40 A Common Everyday Reliability Block Diagram solved R A =.8 R D =.95 R E =.7 R B =.9 R C =.9.81 R F =.8 1-(1-.7)(1-.8) =.94 1-(1-.8)(1-.81) =.962 R s = (.962) (.95) (.94) =

41 Summary Series Configuration Parallel Configuration Combined Series-Parallel Configuration High / Low Level Redundancy K out-of-n Redundancy Complex Configurations linked networks Chapter 5 41

Chapter 9 Part II Maintainability

Chapter 9 Part II Maintainability Chapter 9 Part II Maintainability 9.4 System Repair Time 9.5 Reliability Under Preventive Maintenance 9.6 State-Dependent Systems with Repair C. Ebeling, Intro to Reliability & Maintainability Chapter

More information

CHAPTER 10 RELIABILITY

CHAPTER 10 RELIABILITY CHAPTER 10 RELIABILITY Failure rates Reliability Constant failure rate and exponential distribution System Reliability Components in series Components in parallel Combination system 1 Failure Rate Curve

More information

Quiz #2 A Mighty Fine Review

Quiz #2 A Mighty Fine Review Quiz #2 A Mighty Fine Review February 27: A reliable adventure; a day like all days filled with those events that alter and change the course of history and you will be there! What is a Quiz #2? Three

More information

EE 445 / 850: Final Examination

EE 445 / 850: Final Examination EE 445 / 850: Final Examination Date and Time: 3 Dec 0, PM Room: HLTH B6 Exam Duration: 3 hours One formula sheet permitted. - Covers chapters - 5 problems each carrying 0 marks - Must show all calculations

More information

Engineering Risk Benefit Analysis

Engineering Risk Benefit Analysis Engineering Risk Benefit Analysis 1.155, 2.943, 3.577, 6.938, 10.816, 13.621, 16.862, 22.82, ESD.72, ESD.721 RPRA 3. Probability Distributions in RPRA George E. Apostolakis Massachusetts Institute of Technology

More information

Modeling and Simulation of Communication Systems and Networks Chapter 1. Reliability

Modeling and Simulation of Communication Systems and Networks Chapter 1. Reliability Modeling and Simulation of Communication Systems and Networks Chapter 1. Reliability Prof. Jochen Seitz Technische Universität Ilmenau Communication Networks Research Lab Summer 2010 1 / 20 Outline 1 1.1

More information

Chapter 5. System Reliability and Reliability Prediction.

Chapter 5. System Reliability and Reliability Prediction. Chapter 5. System Reliability and Reliability Prediction. Problems & Solutions. Problem 1. Estimate the individual part failure rate given a base failure rate of 0.0333 failure/hour, a quality factor of

More information

Reliability Engineering I

Reliability Engineering I Happiness is taking the reliability final exam. Reliability Engineering I ENM/MSC 565 Review for the Final Exam Vital Statistics What R&M concepts covered in the course When Monday April 29 from 4:30 6:00

More information

Reliable Computing I

Reliable Computing I Instructor: Mehdi Tahoori Reliable Computing I Lecture 5: Reliability Evaluation INSTITUTE OF COMPUTER ENGINEERING (ITEC) CHAIR FOR DEPENDABLE NANO COMPUTING (CDNC) National Research Center of the Helmholtz

More information

Chapter 7 Physical Reliability Models

Chapter 7 Physical Reliability Models Chapter 7 Physical Reliability Models Periodic Loads Random Loads Physics of Failure Reliability just has to be my favorite subject! C. Ebeling, Intro to Reliability & Maintainability Engineering, Chapter

More information

Statistical Quality Control IE 3255 Spring 2005 Solution HomeWork #2

Statistical Quality Control IE 3255 Spring 2005 Solution HomeWork #2 Statistical Quality Control IE 3255 Spring 25 Solution HomeWork #2. (a)stem-and-leaf, No of samples, N = 8 Leaf Unit =. Stem Leaf Frequency 2+ 3-3+ 4-4+ 5-5+ - + 7-8 334 77978 33333242344 585958988995

More information

The random counting variable. Barbara Russo

The random counting variable. Barbara Russo The random counting variable Barbara Russo Counting Random Variable } Until now we have seen the point process through two sets of random variables } T i } X i } We introduce a new random variable the

More information

Fundamentals of Reliability Engineering and Applications

Fundamentals of Reliability Engineering and Applications Fundamentals of Reliability Engineering and Applications E. A. Elsayed elsayed@rci.rutgers.edu Rutgers University Quality Control & Reliability Engineering (QCRE) IIE February 21, 2012 1 Outline Part 1.

More information

Non-observable failure progression

Non-observable failure progression Non-observable failure progression 1 Age based maintenance policies We consider a situation where we are not able to observe failure progression, or where it is impractical to observe failure progression:

More information

Signal Handling & Processing

Signal Handling & Processing Signal Handling & Processing The output signal of the primary transducer may be too small to drive indicating, recording or control elements directly. Or it may be in a form which is not convenient for

More information

Chapter 18 Section 8.5 Fault Trees Analysis (FTA) Don t get caught out on a limb of your fault tree.

Chapter 18 Section 8.5 Fault Trees Analysis (FTA) Don t get caught out on a limb of your fault tree. Chapter 18 Section 8.5 Fault Trees Analysis (FTA) Don t get caught out on a limb of your fault tree. C. Ebeling, Intro to Reliability & Maintainability Engineering, 2 nd ed. Waveland Press, Inc. Copyright

More information

System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models

System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models Fatih Cavdur fatihcavdur@uludag.edu.tr March 20, 2012 Introduction Introduction The world of the model-builder

More information

IoT Network Quality/Reliability

IoT Network Quality/Reliability IoT Network Quality/Reliability IEEE PHM June 19, 2017 Byung K. Yi, Dr. of Sci. Executive V.P. & CTO, InterDigital Communications, Inc Louis Kerofsky, PhD. Director of Partner Development InterDigital

More information

Why fault tolerant system?

Why fault tolerant system? Why fault tolerant system? Non Fault-Tolerant System Component 1 Component 2 Component N The reliability block diagram of a series systemeach element of the system must operate correctly for the system

More information

ELE 491 Senior Design Project Proposal

ELE 491 Senior Design Project Proposal ELE 491 Senior Design Project Proposal These slides are loosely based on the book Design for Electrical and Computer Engineers by Ford and Coulston. I have used the sources referenced in the book freely

More information

Chapter 15. System Reliability Concepts and Methods. William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University

Chapter 15. System Reliability Concepts and Methods. William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University Chapter 15 System Reliability Concepts and Methods William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University Copyright 1998-2008 W. Q. Meeker and L. A. Escobar. Based on

More information

Markov Reliability and Availability Analysis. Markov Processes

Markov Reliability and Availability Analysis. Markov Processes Markov Reliability and Availability Analysis Firma convenzione Politecnico Part II: Continuous di Milano e Time Veneranda Discrete Fabbrica State del Duomo di Milano Markov Processes Aula Magna Rettorato

More information

Reliability of Safety-Critical Systems 5.1 Reliability Quantification with RBDs

Reliability of Safety-Critical Systems 5.1 Reliability Quantification with RBDs Reliability of Safety-Critical Systems 5.1 Reliability Quantification with RBDs Mary Ann Lundteigen and Marvin Rausand mary.a.lundteigen@ntnu.no &marvin.rausand@ntnu.no RAMS Group Department of Production

More information

Time-varying failure rate for system reliability analysis in large-scale railway risk assessment simulation

Time-varying failure rate for system reliability analysis in large-scale railway risk assessment simulation Time-varying failure rate for system reliability analysis in large-scale railway risk assessment simulation H. Zhang, E. Cutright & T. Giras Center of Rail Safety-Critical Excellence, University of Virginia,

More information

DESIGN OF PREVENTIVE MAINTENANCE SCHEDULING MODEL FOR DETERIORATING SYSTEMS

DESIGN OF PREVENTIVE MAINTENANCE SCHEDULING MODEL FOR DETERIORATING SYSTEMS DESIGN OF PREVENTIVE MAINTENANCE SCHEDULING MODEL FOR DETERIORATING SYSTEMS P.A. Ozor a, S.O. Onyegegbu Department of Mechanical Engineering, University of Nigeria, Nsukka, NIGERIA. a Email: paul.ozor@unn.edu.ng

More information

Reliability and Availability Simulation. Krige Visser, Professor, University of Pretoria, South Africa

Reliability and Availability Simulation. Krige Visser, Professor, University of Pretoria, South Africa Reliability and Availability Simulation Krige Visser, Professor, University of Pretoria, South Africa Content BACKGROUND DEFINITIONS SINGLE COMPONENTS MULTI-COMPONENT SYSTEMS AVAILABILITY SIMULATION CONCLUSION

More information

Reliability analysis of systems and lattice polynomial description

Reliability analysis of systems and lattice polynomial description Reliability analysis of systems and lattice polynomial description Jean-Luc Marichal University of Luxembourg Luxembourg A basic reference Selected references R. E. Barlow and F. Proschan. Statistical

More information

Concept of Reliability

Concept of Reliability Concept of Reliability Prepared By Dr. M. S. Memon Department of Industrial Engineering and Management Mehran University of Engineering and Technology Jamshoro, Sindh, Pakistan RELIABILITY Reliability

More information

(QALT): Data Analysis and Test Design

(QALT): Data Analysis and Test Design (QALT): Data Analysis and Test Design Huairui(Harry) Guo, Ph.D, CRE, CQE Thanasis Gerokostopoulos, CRE, CQE ASTR 2013, Oct. 9-11, San Diego, CA Qualitative vs. Quantitative ALT (Accelerated Life Tests)

More information

Chapter 6. a. Open Circuit. Only if both resistors fail open-circuit, i.e. they are in parallel.

Chapter 6. a. Open Circuit. Only if both resistors fail open-circuit, i.e. they are in parallel. Chapter 6 1. a. Section 6.1. b. Section 6.3, see also Section 6.2. c. Predictions based on most published sources of reliability data tend to underestimate the reliability that is achievable, given that

More information

QUESTION 6 (16 Marks) A continuous random variable X has a Weibull distribution if it has a probability distribution function given by

QUESTION 6 (16 Marks) A continuous random variable X has a Weibull distribution if it has a probability distribution function given by QUESTION 6 (6 Marks) A continuous random variable X has a Weibull distribution if it has a probability distribution function given by x f ( x) e x / if x otherwise where and are positive constants. The

More information

We are IntechOpen, the first native scientific publisher of Open Access books. International authors and editors. Our authors are among the TOP 1%

We are IntechOpen, the first native scientific publisher of Open Access books. International authors and editors. Our authors are among the TOP 1% We are IntechOpen, the first native scientific publisher of Open Access books 3,350 108,000 1.7 M Open access books available International authors and editors Downloads Our authors are among the 151 Countries

More information

B.H. Far

B.H. Far SENG 521 Software Reliability & Software Quality Chapter 8: System Reliability Department of Electrical & Computer Engineering, University of Calgary B.H. Far (far@ucalgary.ca) http://www.enel.ucalgary.ca/people/far/lectures/seng521

More information

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Fault Tolerant Computing ECE 655

UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering. Fault Tolerant Computing ECE 655 UNIVERSITY OF MASSACHUSETTS Dept. of Electrical & Computer Engineering Fault Tolerant Computing ECE 655 Part 1 Introduction C. M. Krishna Fall 2006 ECE655/Krishna Part.1.1 Prerequisites Basic courses in

More information

Maintenance free operating period an alternative measure to MTBF and failure rate for specifying reliability?

Maintenance free operating period an alternative measure to MTBF and failure rate for specifying reliability? Reliability Engineering and System Safety 64 (1999) 127 131 Technical note Maintenance free operating period an alternative measure to MTBF and failure rate for specifying reliability? U. Dinesh Kumar

More information

System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models

System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models System Simulation Part II: Mathematical and Statistical Models Chapter 5: Statistical Models Fatih Cavdur fatihcavdur@uludag.edu.tr March 29, 2014 Introduction Introduction The world of the model-builder

More information

Slides 8: Statistical Models in Simulation

Slides 8: Statistical Models in Simulation Slides 8: Statistical Models in Simulation Purpose and Overview The world the model-builder sees is probabilistic rather than deterministic: Some statistical model might well describe the variations. An

More information

Quantitative evaluation of Dependability

Quantitative evaluation of Dependability Quantitative evaluation of Dependability 1 Quantitative evaluation of Dependability Faults are the cause of errors and failures. Does the arrival time of faults fit a probability distribution? If so, what

More information

Failure rate in the continuous sense. Figure. Exponential failure density functions [f(t)] 1

Failure rate in the continuous sense. Figure. Exponential failure density functions [f(t)] 1 Failure rate (Updated and Adapted from Notes by Dr. A.K. Nema) Part 1: Failure rate is the frequency with which an engineered system or component fails, expressed for example in failures per hour. It is

More information

STAT 509 Section 3.4: Continuous Distributions. Probability distributions are used a bit differently for continuous r.v. s than for discrete r.v. s.

STAT 509 Section 3.4: Continuous Distributions. Probability distributions are used a bit differently for continuous r.v. s than for discrete r.v. s. STAT 509 Section 3.4: Continuous Distributions Probability distributions are used a bit differently for continuous r.v. s than for discrete r.v. s. A continuous random variable is one for which the outcome

More information

Monte Carlo Simulation for Reliability and Availability analyses

Monte Carlo Simulation for Reliability and Availability analyses Monte Carlo Simulation for Reliability and Availability analyses Monte Carlo Simulation: Why? 2 Computation of system reliability and availability for industrial systems characterized by: many components

More information

Computer Science, Informatik 4 Communication and Distributed Systems. Simulation. Discrete-Event System Simulation. Dr.

Computer Science, Informatik 4 Communication and Distributed Systems. Simulation. Discrete-Event System Simulation. Dr. Simulation Discrete-Event System Simulation Chapter 4 Statistical Models in Simulation Purpose & Overview The world the model-builder sees is probabilistic rather than deterministic. Some statistical model

More information

Chapter 5. Statistical Models in Simulations 5.1. Prof. Dr. Mesut Güneş Ch. 5 Statistical Models in Simulations

Chapter 5. Statistical Models in Simulations 5.1. Prof. Dr. Mesut Güneş Ch. 5 Statistical Models in Simulations Chapter 5 Statistical Models in Simulations 5.1 Contents Basic Probability Theory Concepts Discrete Distributions Continuous Distributions Poisson Process Empirical Distributions Useful Statistical Models

More information

Math Spring Practice for the Second Exam.

Math Spring Practice for the Second Exam. Math 4 - Spring 27 - Practice for the Second Exam.. Let X be a random variable and let F X be the distribution function of X: t < t 2 t < 4 F X (t) : + t t < 2 2 2 2 t < 4 t. Find P(X ), P(X ), P(X 2),

More information

Part 3: Fault-tolerance and Modeling

Part 3: Fault-tolerance and Modeling Part 3: Fault-tolerance and Modeling Course: Dependable Computer Systems 2012, Stefan Poledna, All rights reserved part 3, page 1 Goals of fault-tolerance modeling Design phase Designing and implementing

More information

Module 4-2 Methods of Quantitative Reliability Analysis

Module 4-2 Methods of Quantitative Reliability Analysis Module 4-2 Methods of Quantitative Reliability Analysis Chanan Singh Texas A&M University METHODS OF QUANTITATIVE RELIABILITY ANALYSIS ANALYTICAL METHODS - STATE SPACE USING MARKOV PROCESSES - NETWORK

More information

Practical Applications of Reliability Theory

Practical Applications of Reliability Theory Practical Applications of Reliability Theory George Dodson Spallation Neutron Source Managed by UT-Battelle Topics Reliability Terms and Definitions Reliability Modeling as a tool for evaluating system

More information

Cyber Physical Power Systems Power in Communications

Cyber Physical Power Systems Power in Communications 1 Cyber Physical Power Systems Power in Communications Information and Communications Tech. Power Supply 2 ICT systems represent a noticeable (about 5 % of total t demand d in U.S.) fast increasing load.

More information

Availability. M(t) = 1 - e -mt

Availability. M(t) = 1 - e -mt Availability Availability - A(t) the probability that the system is operating correctly and is available to perform its functions at the instant of time t More general concept than reliability: failure

More information

SUPPLEMENT TO CHAPTER

SUPPLEMENT TO CHAPTER SUPPLEMENT TO CHAPTER 4 Reliability SUPPLEMENT OUTLINE Introduction, 2 Finding Probability of Functioning When Activated, 2 Finding Probability of Functioning for a Given Length of Time, 4 Key Terms, 10

More information

Dependable Computer Systems

Dependable Computer Systems Dependable Computer Systems Part 3: Fault-Tolerance and Modelling Contents Reliability: Basic Mathematical Model Example Failure Rate Functions Probabilistic Structural-Based Modeling: Part 1 Maintenance

More information

Chapter 2 - Survival Models

Chapter 2 - Survival Models 2-1 Chapter 2 - Survival Models Section 2.2 - Future Lifetime Random Variable and the Survival Function Let T x = ( Future lifelength beyond age x of an individual who has survived to age x [measured in

More information

Chapter Learning Objectives. Probability Distributions and Probability Density Functions. Continuous Random Variables

Chapter Learning Objectives. Probability Distributions and Probability Density Functions. Continuous Random Variables Chapter 4: Continuous Random Variables and Probability s 4-1 Continuous Random Variables 4-2 Probability s and Probability Density Functions 4-3 Cumulative Functions 4-4 Mean and Variance of a Continuous

More information

The exponential distribution and the Poisson process

The exponential distribution and the Poisson process The exponential distribution and the Poisson process 1-1 Exponential Distribution: Basic Facts PDF f(t) = { λe λt, t 0 0, t < 0 CDF Pr{T t) = 0 t λe λu du = 1 e λt (t 0) Mean E[T] = 1 λ Variance Var[T]

More information

Reliability analysis of power systems EI2452. Lifetime analysis 7 May 2015

Reliability analysis of power systems EI2452. Lifetime analysis 7 May 2015 Reliability analysis of power systems EI2452 Lifetime analysis 7 May 2015 Agenda Summary of content: Introduction nonreparable/reparable General information about statistical surveys Lifetime data Nonparametric

More information

B.H. Far

B.H. Far SENG 637 Dependability, Reliability & Testing of Software Systems Chapter 3: System Reliability Department of Electrical & Computer Engineering, University of Calgary B.H. Far (far@ucalgary.ca) http://www.enel.ucalgary.ca/people/far/lectures/seng637/

More information

MFE634. Reliability Analysis. Group 5. Xiangyu Luo Ameya Palekar Krutin Parashuram Patil Chao Sa

MFE634. Reliability Analysis. Group 5. Xiangyu Luo Ameya Palekar Krutin Parashuram Patil Chao Sa MFE634 Reliability Analysis Group 5 Xiangyu Luo Ameya Palekar Krutin Parashuram Patil Chao Sa Contents. Generate Data and Verify.... Calculate and Analyze the Data.... Complete Sample...... 95% Confidence

More information

Terminology and Concepts

Terminology and Concepts Terminology and Concepts Prof. Naga Kandasamy 1 Goals of Fault Tolerance Dependability is an umbrella term encompassing the concepts of reliability, availability, performability, safety, and testability.

More information

STAT/MATH 395 A - PROBABILITY II UW Winter Quarter Moment functions. x r p X (x) (1) E[X r ] = x r f X (x) dx (2) (x E[X]) r p X (x) (3)

STAT/MATH 395 A - PROBABILITY II UW Winter Quarter Moment functions. x r p X (x) (1) E[X r ] = x r f X (x) dx (2) (x E[X]) r p X (x) (3) STAT/MATH 395 A - PROBABILITY II UW Winter Quarter 07 Néhémy Lim Moment functions Moments of a random variable Definition.. Let X be a rrv on probability space (Ω, A, P). For a given r N, E[X r ], if it

More information

A Probability Primer. A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes.

A Probability Primer. A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes. A Probability Primer A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes. Are you holding all the cards?? Random Events A random event, E,

More information

Semiconductor Technologies

Semiconductor Technologies UNIVERSITI TUNKU ABDUL RAHMAN Semiconductor Technologies Quality Control Dr. Lim Soo King 1/2/212 Chapter 1 Quality Control... 1 1. Introduction... 1 1.1 In-Process Quality Control and Incoming Quality

More information

Variability within multi-component systems. Bayesian inference in probabilistic risk assessment The current state of the art

Variability within multi-component systems. Bayesian inference in probabilistic risk assessment The current state of the art PhD seminar series Probabilistics in Engineering : g Bayesian networks and Bayesian hierarchical analysis in engeering g Conducted by Prof. Dr. Maes, Prof. Dr. Faber and Dr. Nishijima Variability within

More information

MAS113 Introduction to Probability and Statistics. Proofs of theorems

MAS113 Introduction to Probability and Statistics. Proofs of theorems MAS113 Introduction to Probability and Statistics Proofs of theorems Theorem 1 De Morgan s Laws) See MAS110 Theorem 2 M1 By definition, B and A \ B are disjoint, and their union is A So, because m is a

More information

COMPARISON OF RELATIVE RISK FUNCTIONS OF THE RAYLEIGH DISTRIBUTION UNDER TYPE-II CENSORED SAMPLES: BAYESIAN APPROACH *

COMPARISON OF RELATIVE RISK FUNCTIONS OF THE RAYLEIGH DISTRIBUTION UNDER TYPE-II CENSORED SAMPLES: BAYESIAN APPROACH * Jordan Journal of Mathematics and Statistics JJMS 4,, pp. 6-78 COMPARISON OF RELATIVE RISK FUNCTIONS OF THE RAYLEIGH DISTRIBUTION UNDER TYPE-II CENSORED SAMPLES: BAYESIAN APPROACH * Sanku Dey ABSTRACT:

More information

applications of integration 2

applications of integration 2 Contents 15 applications of integration 1. Integration of vectors. Calculating centres of mass 3. Moment of inertia Learning outcomes In this workbook you will learn to interpret an integral as the limit

More information

Chapter 2. 1 From Equation 2.10: P(A 1 F) ˆ P(A 1)P(F A 1 ) S i P(F A i )P(A i ) The denominator is

Chapter 2. 1 From Equation 2.10: P(A 1 F) ˆ P(A 1)P(F A 1 ) S i P(F A i )P(A i ) The denominator is Chapter 2 1 From Equation 2.10: P(A 1 F) ˆ P(A 1)P(F A 1 ) S i P(F A i )P(A i ) The denominator is 0:3 0:0001 0:01 0:005 0:001 0:002 0:0002 0:04 ˆ 0:00009 P(A 1 F) ˆ 0:0001 0:3 ˆ 0:133: 0:00009 Similarly

More information

Combinational Techniques for Reliability Modeling

Combinational Techniques for Reliability Modeling Combinational Techniques for Reliability Modeling Prof. Naga Kandasamy, ECE Department Drexel University, Philadelphia, PA 19104. January 24, 2009 The following material is derived from these text books.

More information

CHAPTER 3 ANALYSIS OF RELIABILITY AND PROBABILITY MEASURES

CHAPTER 3 ANALYSIS OF RELIABILITY AND PROBABILITY MEASURES 27 CHAPTER 3 ANALYSIS OF RELIABILITY AND PROBABILITY MEASURES 3.1 INTRODUCTION The express purpose of this research is to assimilate reliability and its associated probabilistic variables into the Unit

More information

Fault-Tolerant Computing

Fault-Tolerant Computing Fault-Tolerant Computing Motivation, Background, and Tools Slide 1 About This Presentation This presentation has been prepared for the graduate course ECE 257A (Fault-Tolerant Computing) by Behrooz Parhami,

More information

1. Reliability and survival - basic concepts

1. Reliability and survival - basic concepts . Reliability and survival - basic concepts. Books Wolstenholme, L.C. "Reliability modelling. A statistical approach." Chapman & Hall, 999. Ebeling, C. "An introduction to reliability & maintainability

More information

b. ( ) ( ) ( ) ( ) ( ) 5. Independence: Two events (A & B) are independent if one of the conditions listed below is satisfied; ( ) ( ) ( )

b. ( ) ( ) ( ) ( ) ( ) 5. Independence: Two events (A & B) are independent if one of the conditions listed below is satisfied; ( ) ( ) ( ) 1. Set a. b. 2. Definitions a. Random Experiment: An experiment that can result in different outcomes, even though it is performed under the same conditions and in the same manner. b. Sample Space: This

More information

Maintenance Modeling and Optimization. J.J. Arts. Beta Working Paper series 526

Maintenance Modeling and Optimization. J.J. Arts. Beta Working Paper series 526 Maintenance Modeling and Optimization J.J. Arts Beta Working Paper series 526 BETA publicatie WP 526 (working paper) ISBN ISSN NUR Eindhoven March 217 Maintenance Modeling and Optimization Joachim Arts

More information

MAS113 Introduction to Probability and Statistics. Proofs of theorems

MAS113 Introduction to Probability and Statistics. Proofs of theorems MAS113 Introduction to Probability and Statistics Proofs of theorems Theorem 1 De Morgan s Laws) See MAS110 Theorem 2 M1 By definition, B and A \ B are disjoint, and their union is A So, because m is a

More information

CHAPTER 3 MATHEMATICAL AND SIMULATION TOOLS FOR MANET ANALYSIS

CHAPTER 3 MATHEMATICAL AND SIMULATION TOOLS FOR MANET ANALYSIS 44 CHAPTER 3 MATHEMATICAL AND SIMULATION TOOLS FOR MANET ANALYSIS 3.1 INTRODUCTION MANET analysis is a multidimensional affair. Many tools of mathematics are used in the analysis. Among them, the prime

More information

Transient Behavior of

Transient Behavior of Transient Behavior of Static Fault Current Limiter in Distribution System by Shahram Najafi, Vijay K. Sood University of Ontario Institute of Technology, Oshawa, Ontario Electrical Power and Energy Conference

More information

Asymptotic Confidence Limits for a Repairable System with Standbys Subject to Switching Failures

Asymptotic Confidence Limits for a Repairable System with Standbys Subject to Switching Failures American Journal of Applied Sciences 4 (11): 84-847, 007 ISSN 1546-99 007 Science Publications Asymptotic Confidence Limits for a Repairable System with Stbys Subject to Switching Failures 1 Jau-Chuan

More information

Unit 10: Planning Life Tests

Unit 10: Planning Life Tests Unit 10: Planning Life Tests Ramón V. León Notes largely based on Statistical Methods for Reliability Data by W.Q. Meeker and L. A. Escobar, Wiley, 1998 and on their class notes. 11/2/2004 Unit 10 - Stat

More information

Reliability Analysis of Moog Ultrasonic Air Bubble Detectors

Reliability Analysis of Moog Ultrasonic Air Bubble Detectors Reliability Analysis of Moog Ultrasonic Air Bubble Detectors Air-in-line sensors are vital to the performance of many of today s medical device applications. The reliability of these sensors should be

More information

ECE 510 Lecture 6 Confidence Limits. Scott Johnson Glenn Shirley

ECE 510 Lecture 6 Confidence Limits. Scott Johnson Glenn Shirley ECE 510 Lecture 6 Confidence Limits Scott Johnson Glenn Shirley Concepts 28 Jan 2013 S.C.Johnson, C.G.Shirley 2 Statistical Inference Population True ( population ) value = parameter Sample Sample value

More information

Friis Transmission Equation and Radar Range Equation 8.1 Friis Transmission Equation

Friis Transmission Equation and Radar Range Equation 8.1 Friis Transmission Equation Friis Transmission Equation and Radar Range Equation 8.1 Friis Transmission Equation Friis transmission equation is essential in the analysis and design of wireless communication systems. It relates the

More information

Slides 5: Random Number Extensions

Slides 5: Random Number Extensions Slides 5: Random Number Extensions We previously considered a few examples of simulating real processes. In order to mimic real randomness of events such as arrival times we considered the use of random

More information

Reliability of Technical Systems

Reliability of Technical Systems Main Topics 1. Introduction, Key Terms, Framing the Problem 2. Reliability Parameters: Failure Rate, Failure Probability, etc. 3. Some Important Reliability Distributions 4. Component Reliability 5. Software

More information

A STUDY OF ASYMPTOTIC AVAILABILITY MODELING FOR A FAILURE AND A REPAIR RATES FOLLOWING A WEIBULL DISTRIBUTION

A STUDY OF ASYMPTOTIC AVAILABILITY MODELING FOR A FAILURE AND A REPAIR RATES FOLLOWING A WEIBULL DISTRIBUTION A STUDY OF ASYMPTOTIC AVAILABILITY MODELING FOR A FAILURE AND A REPAIR RATES FOLLOWING A WEIBULL DISTRIBUTION Salem Bahri a, Fethi Ghribi b, Habib Ben Bacha a,c a Electro Mechanical systems laboratory

More information

Chapter 9. Bootstrap Confidence Intervals. William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University

Chapter 9. Bootstrap Confidence Intervals. William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University Chapter 9 Bootstrap Confidence Intervals William Q. Meeker and Luis A. Escobar Iowa State University and Louisiana State University Copyright 1998-2008 W. Q. Meeker and L. A. Escobar. Based on the authors

More information

Basics of Monte Carlo Simulations: practical exercises

Basics of Monte Carlo Simulations: practical exercises Basics of Monte Carlo Simulations: practical exercises Piero Baraldi Politecnico di Milano, Energy Department Phone: +39 02 2399 6345 Piero.baraldi@polimi.it EXERCISE 1 (first part) Exercise 1 Consider

More information

CS 1538: Introduction to Simulation Homework 1

CS 1538: Introduction to Simulation Homework 1 CS 1538: Introduction to Simulation Homework 1 1. A fair six-sided die is rolled three times. Let X be a random variable that represents the number of unique outcomes in the three tosses. For example,

More information

Estimation of reliability parameters from Experimental data (Parte 2) Prof. Enrico Zio

Estimation of reliability parameters from Experimental data (Parte 2) Prof. Enrico Zio Estimation of reliability parameters from Experimental data (Parte 2) This lecture Life test (t 1,t 2,...,t n ) Estimate θ of f T t θ For example: λ of f T (t)= λe - λt Classical approach (frequentist

More information

Parametric Failures in COTS Capacitors

Parametric Failures in COTS Capacitors NASA Electronic Parts and Packaging (NEPP) Program Parametric Failures in COTS Capacitors Alexander Teverovsky*, Michael Sampson ** *ASRC AS&D, Inc. work performed for NASA GSFC Code 562 ** NEPP program

More information

Ching-Han Hsu, BMES, National Tsing Hua University c 2015 by Ching-Han Hsu, Ph.D., BMIR Lab. = a + b 2. b a. x a b a = 12

Ching-Han Hsu, BMES, National Tsing Hua University c 2015 by Ching-Han Hsu, Ph.D., BMIR Lab. = a + b 2. b a. x a b a = 12 Lecture 5 Continuous Random Variables BMIR Lecture Series in Probability and Statistics Ching-Han Hsu, BMES, National Tsing Hua University c 215 by Ching-Han Hsu, Ph.D., BMIR Lab 5.1 1 Uniform Distribution

More information

B.H. Far

B.H. Far SENG 521 Software Reliability & Software Quality Chapter 6: Software Reliability Models Department of Electrical & Computer Engineering, University of Calgary B.H. Far (far@ucalgary.ca) http://www.enel.ucalgary.ca/people/far/lectures/seng521

More information

Contents. Introduction Field Return Rate Estimation Process Used Parameters

Contents. Introduction Field Return Rate Estimation Process Used Parameters Contents Introduction Field Return Rate Estimation Process Used Parameters Parts Count Reliability Prediction Accelerated Stress Testing Maturity Level Field Return Rate Indicator Function Field Return

More information

Example: sending one bit of information across noisy channel. Effects of the noise: flip the bit with probability p.

Example: sending one bit of information across noisy channel. Effects of the noise: flip the bit with probability p. Lecture 20 Page 1 Lecture 20 Quantum error correction Classical error correction Modern computers: failure rate is below one error in 10 17 operations Data transmission and storage (file transfers, cell

More information

System Reliability Simulation and Optimization by Component Reliability Allocation

System Reliability Simulation and Optimization by Component Reliability Allocation System Reliability Simulation and Optimization by Component Reliability Allocation Zissimos P. Mourelatos Professor and Head Mechanical Engineering Department Oakland University Rochester MI 48309 Report

More information

Chapter 2. Poisson Processes. Prof. Shun-Ren Yang Department of Computer Science, National Tsing Hua University, Taiwan

Chapter 2. Poisson Processes. Prof. Shun-Ren Yang Department of Computer Science, National Tsing Hua University, Taiwan Chapter 2. Poisson Processes Prof. Shun-Ren Yang Department of Computer Science, National Tsing Hua University, Taiwan Outline Introduction to Poisson Processes Definition of arrival process Definition

More information

Stat 475 Life Contingencies I. Chapter 2: Survival models

Stat 475 Life Contingencies I. Chapter 2: Survival models Stat 475 Life Contingencies I Chapter 2: Survival models The future lifetime random variable Notation We are interested in analyzing and describing the future lifetime of an individual. We use (x) to denote

More information

Software Reliability & Testing

Software Reliability & Testing Repairable systems Repairable system A reparable system is obtained by glueing individual non-repairable systems each around a single failure To describe this gluing process we need to review the concept

More information

Chapter 4 Parametric Families of Lifetime Distributions

Chapter 4 Parametric Families of Lifetime Distributions Chapter 4 Parametric Families of Lifetime istributions In this chapter, we present four parametric families of lifetime distributions Weibull distribution, gamma distribution, change-point model, and mixture

More information

Review of Probabilities and Basic Statistics

Review of Probabilities and Basic Statistics Alex Smola Barnabas Poczos TA: Ina Fiterau 4 th year PhD student MLD Review of Probabilities and Basic Statistics 10-701 Recitations 1/25/2013 Recitation 1: Statistics Intro 1 Overview Introduction to

More information

Lecture 7. Poisson and lifetime processes in risk analysis

Lecture 7. Poisson and lifetime processes in risk analysis Lecture 7. Poisson and lifetime processes in risk analysis Jesper Rydén Department of Mathematics, Uppsala University jesper.ryden@math.uu.se Statistical Risk Analysis Spring 2014 Example: Life times of

More information

3 Modeling Process Quality

3 Modeling Process Quality 3 Modeling Process Quality 3.1 Introduction Section 3.1 contains basic numerical and graphical methods. familiar with these methods. It is assumed the student is Goal: Review several discrete and continuous

More information