Chap 2: Reliability and Availability Models

Similar documents
Control Systems (Lecture note #6)

8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system

Lecture 12: Introduction to nonlinear optics II.

Improvement of the Reliability of a Series-Parallel System Subject to Modified Weibull Distribution with Fuzzy Parameters

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution

MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)

Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS

FAULT TOLERANT SYSTEMS

Chapter 5 Transient Analysis

Reliability Analysis. Basic Reliability Measures

Phys Nov. 3, 2017 Today s Topics. Continue Chapter 2: Electromagnetic Theory, Photons, and Light Reading for Next Time

State Observer Design

The Poisson Process Properties of the Poisson Process

Reliability of time dependent stress-strength system for various distributions

Frequency Response. Response of an LTI System to Eigenfunction

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Introduction to logistic regression

Fourier Series: main points

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

Poisson Arrival Process

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

Advanced Queueing Theory. M/G/1 Queueing Systems

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces

Continuous Time Markov Chains

The Variance-Covariance Matrix

Continous system: differential equations

(1) Cov(, ) E[( E( ))( E( ))]

Homework: Introduction to Motion

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder

Physics 160 Lecture 3. R. Johnson April 6, 2015

Consider a system of 2 simultaneous first order linear equations

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair

Poisson Arrival Process

Introduction to Laplace Transforms October 25, 2017

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis

Numerical Method: Finite difference scheme

Correlation in tree The (ferromagnetic) Ising model

Two-Dimensional Quantum Harmonic Oscillator

Safety and Reliability of Embedded Systems. (Sicherheit und Zuverlässigkeit eingebetteter Systeme) Stochastic Reliability Analysis

CSE 245: Computer Aided Circuit Simulation and Verification

14. Poisson Processes

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

Lecture Y4: Computational Optics I

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia

1973 AP Calculus BC: Section I

Note 6 Frequency Response

Multi-fluid magnetohydrodynamics in the solar atmosphere

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems

The Linear Regression Of Weighted Segments

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

t=0 t>0: + vr - i dvc Continuation

Unbalanced Panel Data Models

NAME: ANSWER KEY DATE: PERIOD. DIRECTIONS: MULTIPLE CHOICE. Choose the letter of the correct answer.

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Mathematical Preliminaries for Transforms, Subbands, and Wavelets

RELIABILITY STOCHASTIC MODELING FOR REPAIRABLE PHYSICAL ASSETS. CASE STUDY APPLIED TO THE CHILEAN MINING

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

4.1 The Uniform Distribution Def n: A c.r.v. X has a continuous uniform distribution on [a, b] when its pdf is = 1 a x b

Midterm exam 2, April 7, 2009 (solutions)

XV Exponential and Logarithmic Functions

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview

u 3 = u 3 (x 1, x 2, x 3 )

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Microscopic Flow Characteristics Time Headway - Distribution

Redundancy System Fault Sampling Under Imperfect Maintenance

ECEN620: Network Theory Broadband Circuit Design Fall 2014

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Lecture 23. Multilayer Structures

9. Simple Rules for Monetary Policy

From Fourier Series towards Fourier Transform

On the Existence and uniqueness for solution of system Fractional Differential Equations

S.Y. B.Sc. (IT) : Sem. III. Applied Mathematics. Q.1 Attempt the following (any THREE) [15]

Chapter 4. Continuous Time Markov Chains. Babita Goyal

On the Class of New Better than Used. of Life Distributions

Southern Taiwan University

Institute of Actuaries of India

Chapter 13 Laplace Transform Analysis

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Economics 302 (Sec. 001) Intermediate Macroeconomic Theory and Policy (Spring 2011) 3/28/2012. UW Madison

Boyce/DiPrima 9 th ed, Ch 2.1: Linear Equations; Method of Integrating Factors

(Reference: sections in Silberberg 5 th ed.)

Inference on Curved Poisson Distribution Using its Statistical Curvature

T h e C S E T I P r o j e c t

Chain DOUBLE PITCH TYPE RS TYPE RS POLY-STEEL TYPE

Convolution of Generated Random Variable from. Exponential Distribution with Stabilizer Constant

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

9.5 Complex variables

3.4 Properties of the Stress Tensor

Almost unbiased exponential estimator for the finite population mean

Transcription:

Chap : lably ad valably Modls lably = prob{s s fully fucog [,]} Suppos from [,] m prod, w masur ou of N compos, of whch N : # of compos oprag corrcly a m N f : # of compos whch hav fald a m rlably of h compo N N Nf Nf N N N d dnf d N d dnf d or N d d Physcal mag: saaous ra a whch compos ar falg 4

How may ufalg compos ar hr a m? N Z * N Falg ra of a sgl compo dn d f N N N f d N f +d - N f Z N N dn f d N d d d d f +d m.3 x p.8 Z s also calld h hazard ra. 5

For lcroc compos, Z s rlaoshp wh rspc o m s a bahub curv. Z Hgh falur ra du o fauly dsg, maufacurg or assmbly. Wak compos ar rmovd durg h bur- prod. Bur- prod Ifa moraly phas Usful m phas Falur ra ca b assumd o b a cosa durg h usful m prod, say War-ou phas du o agg m 6

d d d d Expoal Falur Law.g. =. hr -, wha s a = hrs? s: -.* For hardwar compos, xpoal falur law s frquly assumd. For sofwar compos, h rlably may grow as h sofwar s dsg fauls ar rmovd durg h sg/dbuggg phas. 7

I gral, w ca assum Z = - Wbull = - ds. Ths ylds Z Us hs o modl sofwar falur ra.g. = - - = < > =,, lably mprovs as a fuco of 8

Formal Dfo of : L x b a.. rprsg h lf of a sysm ad l F b h cumulav dsrbuo fuco CDF of x. Th, pr x F For a compo obyg h xpoal falur law f x dx x dx 9

Ma Tm o Falur MTTF: Th xpcd m ha a sysm wll opra bfor h frs falur occurs Dscr cas MTTF N frs falg m Idcal sysms Q: wha s h rlably of a sysm obyg h xpoal falur law a = MTTF? s:.3678... MTTF Couous cas MTTF falur m. g. E T d d d MTTF d f d d N df d d d as d.78... N d d uv' d uv u' vd 3

MTT Ma Tm o par If also assum a fald sysm obys Expoal par Law, h MTT, whr s h rpar ra laoshp bw MTBF Ma m bw falur, MTT & MTTF: m MTTF MTT MTTF MTT Tm of frs falur Tm of d falur MTBF = MTT + MTTF If MTTF >> MTT Th MTBF MTTF 3

valably Isaaous or po valably = prob {h sysm s fucog a m } gardlss of h # of ms may hav fald & b rpard durg [, ] m Sady-Sa valably = MTTF MTTF+MTT For a sysm whou rpar, = 3

ssum xpoal falur & rpar law O F S also pag 67, x chapr 4 Tm doma: P o = - P o + P F P F = P o - P F wh al sa P o = & P F = Laplac doma: P o SP o S - = - P o S + P F S SP F S - = P o S - P F S P F 33

P o S Smlarly P F S P o = P F = = S + S + + S S S + + Physcal mag: - -+ + S S + + -+ Ivrs LT o rur o m doma S F a S LT B Ivrs LT LF = fs /S /S!/S + /S-a P o = prob {h sysm s fucog a m } 34

Q: uavalably? s: P F Q:? Sll - Q3: Sady-sa avalably? s: = + MTTF MTTF MTT.g. =. & =...9.999... 35

Modlg: Srs-Paralll lably Block Dagrams srs-paralll block dagram rprss h logcal srucur of a sysm wh rgard o how h rlabls of s compos affc h sysm rlably. Compos ar combd o blocks srs paralll or k-of- cofguraos 36

37. Sral sysm: ach lm of h sysm s rqurd o fuco corrcly for h sysm o fuco corrcly. 3 srs...... srs B. Paralll sysm: oly o of svral lms mus b opraoal for h sysm o b opraoal. ssumpos: dpd radom varabls prfc covrag so up o - falurs ca b olrad. paralll paralll x F F F :

C. Combao of srs & paralll sysms.g. Compur Irfac Dsplay Bus Compur Irfac Dsplay Bus srs Paralll 3 4 4 4 j, paralll j Numrcal x: =.9 h sysm = [--.9 ] 4 =.96 v.s. o-rduda =.9 4 =.656, Whr j, s for jh compo of h u 38

.g. 3 3 paralll = - - srs, - srs, - srs, 3 = - - - 3-3 Q: Prov h followg horm: plcao a h compo lvl s mor ffcv ha rplcao a h sysm lvl mprovg sysm rlably usg h sam # of compos. s br ha s: Show ha ssum =/ for ach compo sysm 9 6 sysm 7 6 39

D. k-ou-of-.g. TM Trpl Modul dudacy s a -ou-of-3 sysm. TM = * * 3 + - 3 + 3 - + 3 - wh = = 3 = -ou-of-3 = 3-3 I gral,.g. k ou of all ar fucog fald & ar fucog 3 k 3 3 3! 3!! 3 3 3 3 3 ou of 3 4

Q: Is TM > sysm wh a sgl compo? L 3-3 =. sysm TM - 3 +.5 = =. or.5 sgl TM = wh h rlably of a Mor sgl modul s. or.5 ralsc rgo I fac, wh <.5, > TM.5. Q: wha s h MTTF of a k-ou-of- sysm wh ach sgl modul follows h xpoal falur law wh a falur ra of?.g.!!! sysm k MTTF sysm d... k -ou-of-3: MTTF= 3 -ou-of-5: MTTF= 5 4 3 5 6 77 6 4

Q: wha s h rlably & MTTF of h followg srucur? Fg..5, p36 lso a paralll sysm P P p =.764/day -ou-of- m m m 3 m =.39/day -ou-of-3 paralll sysm oo sysm = [ - - -.764 ][ - - -.39 3 ] = 6 -.93-3 -.4-6 -.667 +3 -.86 + -.43 - -.57 MTTF = sysm d = 6.93-3 - 6 3 +.4.667.86.43 call ha par ra Falur ra of compo = Equaos & 3 obad abov ca also b usd o compu h sysm avalably by rplacg wh + - = 6.9.57 4

43 Spcfcally, For sady sa avalably Us = + o quaos 6 5 4 & abov k of ou k paralll srs 4 5 6 ssumg...