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EMIS 7300 SYSTEMS ANALYSIS METHODS FALL 2005 Dr. John Lipp Copyright © 2005 Dr. John Lipp Reliability Definitions • Reliability is the probability that an item will successfully perform its intended function – In general, reliability is a function of time, R(t). – Un-reliability is denoted Q(t) = 1 – R(t). • Reliability / un-reliability are related to the CDF – R(t) = P(working up to time t) = 1 – F(t). – Q(t) = P(failure before time t) = F(t). • Note that R(t) + Q(t) = 1. EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-2 Reliability Definitions (cont.) • The probability of failure in a given time interval, t1 to t2, can be expressed in terms of either reliability or unreliability functions, i.e., P(t1 < T < t2) = R(t1) - R(t2) = Q(t2) - Q(t1) EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-3 Reliability Definitions (cont.) • The mean time to failure (MTTF) is the preferred term when a device is not repairable MTTF R(t )dt 0 • The term mean time between failure (MTBF) is preferred when a device can be repaired MTBF R (t )dt 0 EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-4 Hazard Rate • The hazard rate is the instantaneous probability that a part that has survived to time t will suddenly fail dQ(t ) / dt f (t ) h(t ) R(t ) 1 F (t ) • Typically, h(t) is high during infancy and old age “Burn-in” “Old age” 1 EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-5 Hazard Rate (cont.) • The instantaneous failure rate, h(t), has the following properties: h(t) 0 , t 0 t lim h( )d t 0 t R(t ) e EMIS 7300 Fall 2005 h ( ) d 0 Copyright 2005 Dr. John Lipp S4P1-6 Series Reliability • Independent, series connected devices with reliabilities r1, r2, r3, etc. have an overall reliability of r = r1 r2 r3 … rN. Device 1: r1 Device 2: r2 Device: r … Device 3: r3 = Device N: rN EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-7 Parallel Reliability Device 1: q1 Device 2: q2 Device: q … Device 3: q3 = Device N: qN • Independent, parallel connected devices with un-reliabilities q1, q2, q3, etc. have overall un-reliability q = q1 q2 q3 … qN. • Equivalent to r = 1 – (1 – r1)(1 – r2)(1 – r3) … (1 – rN). EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-8 Exponential Reliability • The most basic model of reliability is the situation where the failure rate is constant over time. • The result is an exponential model, R(t) = e-lt Q(t) = 1 – e-lt h(t) = l MTTF / MTBF = 1/l • Applies when the failure mechanism is simple . • Recall that the exponential distribution is memoryless • The Exponential Model is most often associated with electronic equipment. EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-9 Exponential Probability Paper • -log R(t) = lt, that is, a logarithm paper should show a straight line with exponential reliability. Sample correlation coefficient r quality of linear fit ln 0.01 -log R(t) slope = l ln 0.1 ln 1.0 time EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-10 Exponential Parameter Estimation • The parameter l can be estimated with lˆ n 1 t1 t2 t3 tn t or confidence intervals computed via 12 / 2, 2 n 2nt EMIS 7300 Fall 2005 l 2 / 2, 2 n 2nt Copyright 2005 Dr. John Lipp S4P1-11 • Half-life of radioactive material exponential EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-12 Series Exponential Reliability • A series connection of N identical, exponentially reliable components has an overall exponential reliability R(t ) R1 (t ) R2 (t ) R3 (t ) RN (t ) e l1t e l2t e l3t e lN t lt e i EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-13 Non-constant Failure Rates Increasing Failure Rate ln 0.01 -log R(t) ln 0.1 Decreasing Failure Rate ln 1.0 time EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-14 Weibull Reliability • When the failure rate isn’t constant with respect to time, the distribution that fits the data is usually the Weibull – Reliability R(t ) e – Hazard Rate h(t ) blb t b 1 lt b • When b < 1 the failure rate is increasing. • When b = 1 the failure rate is constant. • When b > 1 the failure rate is increasing. – MTTF/MTBF 1 1 b Gamma Function l EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-15 Weibull Reliability (cont.) • Weibull is the first choice reliability model – Models “weakest link in the chain” (series). – 20-30 points usually required to discredit. – Note: includes exponential (b = 1). • b is the shape parameter • l is the scale parameter EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-16 Weibull PDF f(t) 1.8 β=5.0 1.6 1.4 1.2 β=0.5 β=3.44 β=1.0 β=2.5 1.0 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 t t is in multiples of EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-17 Weibull Probability Paper • Probability paper for the Weibull is based on the logarithm of the logarithm of the inverse reliability 1 log log b log l b log t R(t ) • That is, – Y = log(failure time) – X = log(log(1 / (1 – Median Rank (Y)))) – Fit Y = mX + b using the least squares method (AKA simple linear regression). – Estimate of l = e-b and b = m-1. EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-18 The Log-Normal Distribution • A log-normal tends to be a model of reliability when – A large number of failures have to occur, that is, an effective model is that of a large, parallel system, the failure tends to have a log-normal distribution. – Non-linear increases in the failure rate of the components. – The log-normal appears concave down on Weibull paper. • The Lognormal Model is often used as the failure distribution for mechanical items and for the distribution of repair times. • If T ~ LN(,), then Y = lnT ~ N(,). EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-19 Homework • Probability and Statistics for Reliability: An Introduction http://quanterion.com/ReliabilityQues/V4N2.html EMIS 7300 Fall 2005 Copyright 2005 Dr. John Lipp S4P1-20