Monte Carlo Simulation for Reliability and Availability analyses
|
|
- Martha Washington
- 5 years ago
- Views:
Transcription
1 Monte Carlo Simulation for Reliability and Availability analyses
2 Monte Carlo Simulation: Why? 2 Computation of system reliability and availability for industrial systems characterized by: many components multistate components complex architectures and production logics complex maintenance policy The use of analytical methods is unfeasible Monte Carlo Simulation
3 A real example of Monte Carlo Simulation PRODUCTION AVAILABILITY EVALUATION OF AN OFFSHORE INSTALLATION Zio, E., Baraldi, P., Patelli E. Assessment of the availability of an offshore installation by Monte Carlo simulation. International Journal of Pressure Vessels and Piping 83 (2006)
4 System description: basic scheme 4 TC TC 50% 50% 60 b TEG Flare Gas Export 3.0 msm3/d, 60b Oil export Wells Separation Oil Trt Wat. Trt Sea Water Inj.
5 System description: gas-lift 5 60 b First loop Gas-lift pressure Production of the Well % 60 80% 0 60% 5
6 System description: fuel gas generation and distribution 6 Second loop
7 System description: electricity power production and distribution 7
8 The offshore production plant 8 TC TC 0.1 MSm MSm MSm3/d 50% 50% 2.2 MSm 3 /d Fuel Gas 25 b Gas Lift 60b 4.4 MSm 3 /d TEG 6 MW 1 MSm 3 /d Flare Gas Export 3.0 MSm 3 /d, 60b Wells Production Gas : 5 MSm 3 /d Oil : Sm 3 / d Water : 8000 Sm 3 /d 4.4 MSm 3 /d Separation 7000 Sm 3 /d Sm 3 /d 0.1 MSm 3 TG 13 MW 50% Gas Lift 100b Oil Trt EC 6 MW 7 MW Oil export Wat. Trt 0.1 MSm 3 TG 50% 13 MW Sea 7 MW Water Inj.
9 Component failures and repairs: TCs and TGs 9 l 02 TC TG l m 10 m 20 l 12 2 l h h -1 l h h -1 l h h -1 m h h -1 m h h -1 State 0 = as good as new State 1 = degraded (no function lost, greater failure rate value) State 2 = critical (function is lost)
10 Component failures and repairs: EC and TEG 10 l EC TEG 0 2 m l h h -1 m h h -1 State 0 = as good as new State 2 = critical (function is lost)
11 Production priority: example 11 TC TC 0.1 MSm MSm MSm3/d 50% 50% 2.2 MSm 3 /d Fuel Gas 25 b Gas Lift 60b 4.4 MSm 3 /d TEG 6 MW 1 MSm 3 /d Flare Gas Export 3.0 MSm 3 /d, 60b 4.4 MSm 3 /d Gas Lift 100b EC Oil export Wells Production Gas : 5 MSm 3 /d Oil : Sm 3 / d Water : 8000 Sm 3 /d Separation 7000 Sm 3 /d Sm 3 /d 0.1 MSm 3 TG 13 MW 50% Oil Trt 7 MW Wat. Trt 0.1 MSm 3 TG 50% 13 MW Sea Water Inj.
12 Production priority 12 When a failure occurs, the system is reconfigured to minimise (in order): the impact on the export oil production the impact on export gas production The impact on water injection does not matter
13 Production priority: example 13 TC TC 0.1 MSm MSm MSm3/d 50% 50% 2.2 MSm 3 /d Fuel Gas 25 b Gas Lift 60b 4.4 MSm 3 /d TEG 6 MW 1 MSm 3 /d Flare Gas Export 3.0 MSm 3 /d, 60b 4.4 MSm 3 /d Gas Lift 100b EC Oil export Wells Production Gas : 5 MSm 3 /d Oil : Sm 3 / d Water : 8000 Sm 3 /d Separation 7000 Sm 3 /d Sm 3 /d 0.1 MSm 3 TG 13 MW 50% Oil Trt 7 MW Wat. Trt 0.1 MSm 3 TG 50% 13 MW Sea Water Inj.
14 Maintenance policy: reparation 14 Only 1 repair team Two or more components are failed at the same time Priority levels of failures: 1. Failures leading to total loss of export oil (both TG s or both TC s or TEG) 2. Failures leading to partial loss of export oil (single TG or EC) 3. Failures leading to no loss of export oil (single TC failure)
15 Maintenance policy: preventive maintenance 15 Only 1 preventive maintenance team The preventive maintenance takes place only if the system is in perfect state of operation Type of maintenance Frequency [hours] Duration [hours] Turbo-Generator and Turbo-Compressors Type (90 days) 4 Type (1 year) 120 (5 days) Type (5 years) 672 (4 weeks) Electro Compressor Type
16 Problem Statement 16 Compute the system istantaneous and average availability The use of analytical methods is unfeasible Monte Carlo simulation
17 Monte Carlo Simulation
18 The History of Monte Carlo Simulation Buffon Kelvin (kinetic theory of gas - multidimensional integrals) Random sampling of numbers Gosset (coefficient of the t-student) Fermi, von Neumann, Ulam (Manhattan project, interaction neutron-matter) Neutron transport (design of shielding in nuclear devices - 6 dimension-integral) System Transport (RAM, ) s 1950 s 1990 s
19 Contents 19 Sampling Random Numbers Simulation of system transport Simulation for reliability/availability analysis of a component Example
20 SAMPLING RANDOM NUMBERS
21 Sampling (pseudo) Random Numbers Uniform Distribution 21 u R (r) 0 pdf : u R r dur dr r 1 U R (r) cdf : U R r P R r r 0
22 Sampling Random Numbers from an Uniform Distribution 22 In the past: The Rand Book (1955) 1 million of random numbers
23 Sampling Random Numbers from an Uniform Distribution 23 Today: R~U[0, 1)
24 Sampling Random Numbers from a Generic Distribution 24 F X (x) 1 x
25 Sampling Random Numbers from a Generic Distribution 25 F X (x) Sample R from U R (r) 1 X F R X 1 R X = F 1 (R) x
26 Sampling Random Numbers from a Generic Distribution 26 Pr R r U r R F X x R 1 Sample R from U R (r) and find X: X F 1 X R r 1 R 0 X x Question: which distribution does X obey? 1 X x P F R P X x F
27 Sampling (pseudo) Random Numbers Generic Distribution 27 Pr R r U r R F X x R 1 Sample R from U R (r) and find X: X F 1 X R r 1 R 0 X x Question: which distribution does X obey? 1 X x P F R P X x F P R F X x
28 Sampling (pseudo) Random Numbers Generic Distribution 28 Pr R r U r R F X x R 1 Sample R from U R (r) and find X: X F 1 X R r 1 R 0 X x Question: which distribution does X obey? 1 X x P F R P X x F P R F x F x X X
29 Sampling (pseudo) Random Numbers Generic Distribution 29 Pr R r U r R F X x R 1 Sample R from U R (r) and find X: X F 1 X R r 1 R 0 X x Question: which distribution does X obey? 1 X x P F R P X Application of the operator F x to the argument of P above yields P x X x P R F x F x Summary: From an R U R (r) we obtain an X F X (x) X X
30 Sampling from a Bernoulli distribution 30 X = 1 healthy 0 failed 0.9 P X (x) with P X = 0 = p 0 = F X (x) p o =0.1 p o = x 0 1 x
31 Sampling from a Bernoulli distribution 31 X = 1 healthy 0 failed 0.9 P X (x) with P X = 0 = p 0 = 0.1 F X (x) R 1 p o =0.1 p o = x 0 1 x Sample R from U R (r) If R p 0 X = 0 If R > p 0 X = 1 Does it work? = R 0 p 0 1 P X = 0 = P R p 0 = p 0 P X = 1 = P R > p 0 = 1 p 0
32 Sampling from discrete distributions 32 Ω = x 0, x 1,, x k P X = x i = f i F i = P X x i i = j=0 f j
33 Sampling from discrete distributions 33 Ω = x 0, x 1,, x k, P X = x k = f k F k = P X x k k = i=0 f k Sampling procedure: Sample an R~ U[0,1) Find i such that F i 1 < R F i Graphically: Why does it work? P X = x i = P F i 1 < R F i = F i F i 1 = f i
34 Exercise 1: Sampling from the Exponential Distribution 34 Probability density function: f T ( t) le lt t 0 0 t 0 f T (t) Expected value and variance: 1 E[ T ] l 1 Var[ T ] 2 l t f () t le l T t Find the procedure to sample numbers from an exponential distribution. Assume to have available a generator of random numbers from an uniform distribution in the interval [0,1)
35 Failure probability estimation: example 35 Arc number i Failure probability P i Calculate the analytic solution for the failure probability of the network, i.e., the probability of no connection between nodes S and T 2- Repeat the calculation with Monte Carlo simulation
36 Analytic solution 36 1) Minimal Failure Configuration (minimal cut set): M 1 ={1,4} M 2 ={2,5} M 3 ={1,3,5} M 4 ={2,3,4} 2) Network failure probability: P[ M ] p p P[ M ] p p P[ M ] p p p P[ M ] p p p P[ X 1] P[ M ] T j 1 j 3 rare event approximation 36
37 Monte Carlo simulation 37 The state of arc i can be sampled by applying the inverse transform method to the set of discrete probabilities pi,1 pi of the mutually exclusive and exhaustive states l B 1 2 B 1 p i l B l1 3 1 B p l1 i S R S R U[0,1) 0 1 We calculate the arrival state for all the arcs of the newtork As a result of these transitions, if the system fails we add 1 to N f The trial simulation then proceeds until we collect M trials. P MC = N f M
38 Monte Carlo simulation 38 N number of trials N F number of trials corresponding to realization of a minimal cut set The failure probability is equal to N F / N clear all; %arc failure probabilities p=[0.05,0.025,0.05,0.02,0.075]; nf=0; M=1e6; %number of MC simulations for i=1:m %sampling of arcs fault events for j=1:5 % simulation of arc states r=rand if (r<=p(j)) s(1,j)=1; % arc j is failed otherwise s(1,j)=0; % arc j is working end end (is the network faulty?) fault=s(i,1)*s(i,4)+s(i,2)*s(i,5)+s(i,1)*s(i,3)*s(i,5)+s(i,2)*s(i,3)*s(i,4); if fault >= 1 nf=nf+1; end end pf=nf/m; %system failure probability For M=10 6, we obtain PX [ T 1]
39 SIMULATION OF SYSTEM TRANSPORT
40 Monte Carlo simulation for system reliability 40 PLANT = system of N c suitably connected components. COMPONENT = a subsystem of the plant (pump, valve,...) which may stay in different exclusive (multi)states (nominal, failed, stand-by,... ). Stochastic transitions from state-to-state occur at stochastic times. STATE of the PLANT at t = the set of the states in which the N c components stay at t. The states of the plant are labeled by a scalar which enumerates all the possible combinations of all the component states. PLANT TRANSITION = when any one of the plant components performs a state transition we say that the plant has performed a transition. The time at which the plant performs the n-th transition is called t n and the plant state thereby entered is called k n. PLANT LIFE = stochastic process.
41 Plant life: random walk 41 Random walk = realization of the system life generated by the underlying state-transition stochastic process. Phase space
42 Phase Space 42
43 Example: System Reliability Estimation 43 Divide the mission time, T M, in bins and associate a counter (of the system failure) to each bin: C F 1, C F 2,, C F τ, Initialize each counter to 0: C F 1 = 0, C F 2, = 0,, C F τ = 0, bin C F 1 = 0 C F 2 = 0 C F τ = 0 0 T M
44 Example: System Reliability Estimation 44 Divide the mission time, T M, in bins and associate a counter (of the system failure) to each bin: C F 1, C F 2,, C F τ, Initialize each counter to 0: C F 1 = 0, C F 2, = 0,, C F τ = 0, C F 1 = 0 C F 2 = 0 C F τ = 0 0 MC simulation of the first system life T M C F 2 = 0 C F 1 = 0 C F τ = 0 0 T M Events at component level, which do not entail system failure No system failure during first simulation C F τ = C F τ for τ [0, T m ]
45 Example: System Reliability Estimation 45 MC simulation of the first system life C F 2 = 0 C F 1 = 0 C F τ = 0 0 MC simulation of the second system life T M C F 1 = 0 CF 2 = 0 C F τ 2 = C F τ 2 +1= τ 2 T M Event at component level, which do not entail system failure Event at component level, which causes the system failure system failure at time τ 2 C F τ = C F τ + 1 for τ > τ 2
46 Example: System Reliability Estimation 46 MC simulation of the first system life C F 2 = 0 C F 1 = 0 C F τ = 0 0 MC simulation of the second system life T M MC simulation of the M-th system life T M τ M Event at component level, which do not entail system failure Event at component level, which causes the system failure T M system failure at time τ M C F τ = C F τ + 1 for τ > τ M
47 Example: System Reliability Estimation 47 MC simulation of the first system life C F 2 = 0 C F 1 = 0 C F τ = 0 0 MC simulation of the second system life T M MC simulation of the M-th system life T M τ M Event at component level, which do not entail system failure Event at component level, which causes the system failure T M MC estimation of the system reliability F T τ CF τ M R τ = 1 CF τ M
48 Example: System Reliability Estimation 48 4 F(t)=1-R(t) 1/4 1/2 t
49 SIMULATION FOR COMPONENT AVAILABILITY ESTIMATION 49
50 One component with exponential distribution of the failure time 50 l 0 m 1 values l h -1 m h -1 State X=0 (WORKING) State X=1 (FAILED) Limit unavailability: q =? MTTR MTTR + MTTF
51 One component with exponential distribution of the failure time 51 0 m l 1 values l h -1 m h -1 State X=0 (WORKING) State X=1 (FAILED) Limit unavailability: q = MTTR MTTR + MTTF = 1 μ 1 μ + 1 λ =
52 One component with exponential distribution of the failure and repair time 52 l X(t) 0 1 m State 1 State 0 State X=0 (WORKING) State X=1 (FAILED)
53 One component with exponential distribution of the failure and repair time 53 l X(t) 0 1 m State 1 State 0 State X=0 (WORKING) State X=1 (FAILED)
54 One component with exponential distribution of the failure and repair time 54 l 0 1 m State 1 State 0 State X=0 (WORKING) State X=1 (FAILED)
55 Monte carlo simulation for estimating the system availability at time t 55 Divide the mission time, T M, in bins and associate a counter (of the system failure) to each bin: C q 1, C q 2,, C q τ, Initialize each counter to 0: C q 1 = 0, C q 2, = 0,, C q τ = 0, If the component is failed in (t j, t j + t), the corrresponding counter, c q (t j ), increases c q (t j ) = c q (t j ) +1; otherwise, c q (t j ) = c q (t j ) Trial 1
56 Monte carlo simulation for estimating the system availability at time t 56 another trial Trial 1 Trial i-th
57 Monte carlo simulation for estimating the system availability at time t 57 another trial Trial 1 Trial i-th Trial M-th
58 Monte carlo simulation for estimating the system availability at time t 58 The counter c q (t j ) adds 1 until M trials have been sampled q t j = P X t j = 1 Cq t j M Trial 1 Trial i-th Trial M-th q t j Cq t j M
59 Unavailability Pseudo Code %Initialize parameters Tm=mission time; M=number of trials; Dt=bin length; Time_axis=0:Dt:Tm; counter_q=zeros(length(1,time_axis)) for i=1:m %parameter initialization for each trial t=0; state=0; %state=working while t<tm if state=0 %system is working sampling of failure time t=t-[log(1-rand)]/lambda %failure time state=1; % state=failed failure_time=t; lower_b=minimum(find(time_axis>=failure_time)); % first unavailability counter to be increased else %system is failed sampling of repair time t=t-[log(1-rand)]/mu; state=1; repair_time=t; upper_b=minimum(find(time_axis > repair time)); %last unavailability counter to be increased counter(lower_b:upper_b)= counter(lower_b:upper_b)+1; %increase all unavailability counter between lower_b and upper_b end end end Unav(:)=counter(:)/M Falta de disponibilidad único Single ensayo trial 0.6 Simulacion Monte Carlo Monte simulation Carlo Falta Limit de unavailability disponibilidad en estado estacionario Time Tiempo
60 SIMULATION FOR SYSTEM AVAILABILITY / RELIABILITY ESTIMATION 60
61 Stochastic Transitions: Governing Probabilities 61 T(t t ; k )dt = conditional probability of a transition at t dt, given that the preceding transition occurred at t and that the state thereby entered was k. C(k k ; t) = conditional probability that the plant enters state k, given that a transition occurred at time t when the system was in state k. Both these probabilities form the trasport kernel : K(t; k t ; k )dt = T(t t ; k )dt C(k k ; t)
62 Phase Space 62
63 Initial Indirect Monte Carlo: Example (1) 63 A C B Components times of transition between states are exponentially distributed i ( l = rate of transition of component i going from its state j i to the state m i ) j i m Arrival AB ( ) - l 2 1 A( B) A 3 l 3 1 l ( B ) l A( B) 1 2 l l A( B) 1 3 A( B) 2 3
64 Initial Indirect Monte Carlo: Example (2) Arrival l1 C 2 l1 C 3 l1 C 4 2 l C 1-2 l C 2 3 l C l C 3 1 l C 2 3 l C l C 4 1 l C l C 3 The components are initially (t=0) in their nominal states (1,1,1) The failure configuration are (C in state 4:(*,*,4)) and (A and B in 3: (3,3,*)).
65 Monte Carlo Trial: Sampling the time of transition 65 How to build T(t t = 0, k = (1,1,1)? The rate of transition of component A(B) out of its nominal state 1 is: l1 A( B) A l1 ( B) 2 A l1 The rate of transition of component C out of its nominal state 1 is: C C l l1 C l1 The rate of transition of the system out of its current configuration (1, 1, 1) is: 1,1,1 l We are now in the position of sampling the first system transition time t 1, by applying the inverse transform method: ( B) 3 C l A B C l1 l1 l1 T(t t = 0, k = 1,1,1 = λ 1,1,1 e λ 1,1,1 t where R t ~ U[0,1) t t 1 l ln(1 ) 1,1,1 t 1 0 R
66 Monte Carlo Trial: Sampling the kind of Transition 66 How to build T(k t = t 1, k = (1,1,1))? Assuming that t 1 < T M (otherwise we would proceed to the successive trial), we now need to determine which transition has occurred, i.e. which component has undergone the transition and to which arrival state. The probabilities of components A, B, C undergoing a transition out of their initial nominal states 1, given that a transition occurs at time t 1, are: l l A 1, l l B 1 1,1,1 1,1,1 1,1,1 Thus, we can apply the inverse transform method to the discrete distribution, l l C 1 P(A ) P(B) P(C ) l = R l1 A C l1 B 1 1,1,1 1,1,1 1,1,1 l = = l l C 0 1
67 Sampling the Kind of Transition (2) 67 Given that at t 1 component B undergoes a transition, its arrival state can be sampled by applying the inverse transform method to the set of discrete probabilities B B l1 2 l1 3, B B l1 l1 of the mutually exclusive and exhaustive arrival states 0 l B 1 2 B 1 l As a result of this first transition, at t 1 the system is operating in configuration (1,3,1). The simulation now proceeds to sampling the next transition time t 2 with the updated transition rate l 1,3,1 l B 1 3 B 1 l A B C l1 l3 l1 S R S 1 R S U[0,1)
68 Sampling the Next Transition 68 The next transition, then, occurs at where R t ~ U[0,1). t t 1 l ln(1 ) 1,3,1 t 2 1 R Assuming again that t 2 < T M, the component undergoing the transition and its final state are sampled as before by application of the inverse trasform method to the appropriate discrete probabilities. The trial simulation then proceeds through the various transitions from one system configuration to another up to the mission time T M.
69 69 Unreliability and Unavailability Estimation When the system enters a failed configuration (*,*,4) or (3,3,*), where the * denotes any state of the component, tallies are appropriately collected for the unreliability and instantaneous unavailability estimates (at discrete times t j [0, T M ]); After performing a large number of trials M, we can obtain estimates of the system unreliability at any time t j by F t j = CF (t j ) M system instantaneous unavailability at any time t j by q t j = Cq (t j ) M
70 Example: System Reliability Estimation 70 Divide the mission time, T M, in bins and associate a counter (of the system failure) to each bin: bin Initialize each counter to 0: C F 1, C F 2,, C F τ, C F 1 = 0 C F 2 = 0 C F 1 = 0, C F 2, = 0,, C F τ = 0, C F τ = 0 0 T M
71 Example: System Reliability Estimation 71 R R CC F τ ( t) = CC F ( tτ) t 0, T M τε[0, T M ] τ 1 τ r1 R R CC F ( tτ ) = CC( F t) τ 1 + t1, τε[τ T M 1, T M ] R R CC ( t) C ( t) 1 t, T τ F τ = C F τ + 1 τε[τ M 2, T M ] r2 τ 2 C F τ = C F τ τε[0, T M ] Events at components level, which do not entail system failure Repair Time F T τ CF τ M R τ = 1 CF τ M
72 Example: System Availability Estimation 72 Divide the mission time, T M, in bins and associate a counter (of the system failure) to each bin: bin Initialize each counter to 0: C q 1, C q 2,, C q τ, C q 1 = 0 C q 2 = 0 C q 1 = 0, C q 2, = 0,, C q τ = 0, C q τ = 0 0 T M
73 Example: System Availability Estimation 73 R R CC q τ ( t) = CC q ( tτ) t 0, T M τε[0, T M ] C q τ = C q τ + 1 τε[τ 1, τ r1 ] τ 1 τ r1 τ 2 τ r2 C q τ = C q τ + 1 τε[τ 1, τ r2 ] C q τ = C q τ τε[0, T M ] Events at components level, which do not entail system failure Repair Time q τ Cq τ M p τ = 1 Cq τ M
74 A real example of Monte Carlo Simulation PRODUCTION AVAILABILITY EVALUATION OF AN OFFSHORE INSTALLATION Zio, E., Baraldi, P., Patelli E. Assessment of the availability of an offshore installation by Monte Carlo simulation. International Journal of Pressure Vessels and Piping 83 (2006)
75 The offshore production plant 75 TC TC 0.1 MSm MSm MSm3/d 50% 50% 2.2 MSm 3 /d Fuel Gas 25 b Gas Lift 60b 4.4 MSm 3 /d TEG 6 MW 1 MSm 3 /d Flare Gas Export 3.0 MSm 3 /d, 60b Wells Production Gas : 5 MSm 3 /d Oil : Sm 3 / d Water : 8000 Sm 3 /d 4.4 MSm 3 /d Separation 7000 Sm 3 /d Sm 3 /d 0.1 MSm 3 TG 13 MW 50% Gas Lift 100b Oil Trt EC 6 MW 7 MW Oil export Wat. Trt 0.1 MSm 3 TG 50% 13 MW Sea 7 MW Water Inj.
76 MONTE CARLO APPROACH Plant state? Production levels oil gas water 76 TG 1 TG 2 TC 1 TC 2 TEG EC Associate a production level to each of the 453 plant states A systematic procedure
77 A systematic procedure 77 7 different production levels Plant state (Production Level) Gas [ksm 3 /d] Oil [k m 3 /d] Water [m 3 /d] 0=(100%) Failure of ony 1 TG Failure of the EC Failure of TEG, Failure of both TCs Failure of both TGs
78 Real system with preventive maintenance 78 Production level Average availability E E E E E E E-2
79 Real system with preventive maintenance 79 OIL Mean Std Oil [k m 3 /d] Gas [k Sm 3 /d] GAS Water [k m 3 /d] WATER
80 Case A: perfect system and preventive maintenances 80 Type of maintenance Frequency [hours] Duration [hours] Turbo-Generator and Turbo-Compressors Type (90 days) 4 Type (1 year) 120 (5 days) Type (5 years) 672 (4 weeks) Electro Compressor Type
81 Case A: perfect system and preventive maintenances 81 P.Maintenance Type 1 (TC,TG) P.Maintenance Type 2 (EC) OIL P.Maintenance Type 3 (TC,TG) Mean Std Oil [k m 3 /d] Gas [k Sm 3 /d] Water [k m 3 /d] GAS WATER
82 Case B: Component Failure and no preventive maintenances 82
83 Conclusions 83 Complex multi-state system with maintenance and operational loops MC simulation Systematic procedure to assign a production level to each configuration oil gas water Investigation of effects maintenance on production
84 Where to study? 84 Slides Book: Chapter 1 Sections 3.1,3.3.1,3.3.2 (no examples of the multivariate normal distribution) Sections 4.1,4.2,4.3,4.4,4.4.1 Section 5.1
A MONTE CARLO SIMULATION APPROACH TO THE PRODUCTION AVAILABILITY EVALUATION OF AN OFFSHORE INSTALLATION WITH OPERATIONAL LOOPS
A MONTE CARLO SIMULATION APPROACH TO THE PRODUCTION AVAILABILITY EVALUATION OF AN OFFSHORE INSTALLATION WITH OPERATIONAL LOOPS ENRICO ZIO, PIERO BARALDI, AND EDOARDO PATELLI Dipartimento Ingegneria Nucleare
More informationBasics 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 informationEE 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 informationMonte Carlo Simulation for Reliability and Availability analyses. November 2016
Mone Carlo Simulaion for Reliabiliy and Availabiliy analyses November 206 The experimenal view Conens 3 Sampling Random Numbers Simulaion of sysem ranspor Simulaion for reliabiliy/availabiliy analysis
More informationAssessment of the Performance of a Fully Electric Vehicle Subsystem in Presence of a Prognostic and Health Monitoring System
A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 33, 2013 Guest Editors: Enrico Zio, Piero Baraldi Copyright 2013, AIDIC Servizi S.r.l., ISBN 978-88-95608-24-2; ISSN 1974-9791 The Italian Association
More informationMarkov 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 informationA 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 informationReliable 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 informationEngineering 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 informationFault-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 informationChapter 4 Availability Analysis by Simulation and Markov Chain
Chapter 4 Availability Analysis by Simulation and Markov Chain Chapter 4 Availability Analysis by Simulation and Markov Chain 4.1 Introduction: For a perfect design, an engineering systems, component and
More informationQuantitative 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 informationLecture 5 Probability
Lecture 5 Probability Dr. V.G. Snell Nuclear Reactor Safety Course McMaster University vgs 1 Probability Basic Ideas P(A)/probability of event A 'lim n64 ( x n ) (1) (Axiom #1) 0 # P(A) #1 (1) (Axiom #2):
More informationReliability of Technical Systems
Reliability of Technical Systems Main Topics 1. Short Introduction, Reliability Parameters: Failure Rate, Failure Probability, etc. 2. Some Important Reliability Distributions 3. Component Reliability
More informationWe 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 informationFault-Tolerant Computer System Design ECE 60872/CS 590. Topic 2: Discrete Distributions
Fault-Tolerant Computer System Design ECE 60872/CS 590 Topic 2: Discrete Distributions Saurabh Bagchi ECE/CS Purdue University Outline Basic probability Conditional probability Independence of events Series-parallel
More informationTerminology 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 informationKey Words: Lifetime Data Analysis (LDA), Probability Density Function (PDF), Goodness of fit methods, Chi-square method.
Reliability prediction based on lifetime data analysis methodology: The pump case study Abstract: The business case aims to demonstrate the lifetime data analysis methodology application from the historical
More information9. Reliability theory
Material based on original slides by Tuomas Tirronen ELEC-C720 Modeling and analysis of communication networks Contents Introduction Structural system models Reliability of structures of independent repairable
More informationReliability 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 information12 - The Tie Set Method
12 - The Tie Set Method Definitions: A tie set V is a set of components whose success results in system success, i.e. the presence of all components in any tie set connects the input to the output in the
More informationAvailability. 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 informationContinuous Random Variables
MATH 38 Continuous Random Variables Dr. Neal, WKU Throughout, let Ω be a sample space with a defined probability measure P. Definition. A continuous random variable is a real-valued function X defined
More informationNuclear reliability: system reliabilty
Nuclear reliability: system reliabilty Dr. Richard E. Turner (ret26@cam.ac.uk) December 3, 203 Goal of these two lectures failures are inevitable: need methods for characterising and quantifying them LAST
More informationReliability 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 informationMonte Carlo Simulation for Reliability Analysis of Emergency and Standby Power Systems
Monte Carlo Simulation for Reliability Analysis of Emergency and Standby Power Systems Chanan Singh, Fellow, IEEE Joydeep Mitra, Student Member, IEEE Department of Electrical Engineering Texas A & M University
More informationKEYWORDS: Importance measures; Repairable systems; Discrete event simulation; Birnbaum; Barlow-Proschan; Natvig
Dept. of Math. University of Oslo Statistical Research Report o. 10 ISS 0806 3842 September 2008 SIMULATIO BASED AALYSIS AD A APPLICATIO TO A OFFSHORE OIL AD GAS PRODUCTIO SYSTEM OF THE ATVIG MEASURES
More informationQuantitative 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 informationTime-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 informationMODELLING DYNAMIC RELIABILITY VIA FLUID PETRI NETS
MODELLING DYNAMIC RELIABILITY VIA FLUID PETRI NETS Daniele Codetta-Raiteri, Dipartimento di Informatica, Università di Torino, Italy Andrea Bobbio, Dipartimento di Informatica, Università del Piemonte
More informationRisk Analysis of Highly-integrated Systems
Risk Analysis of Highly-integrated Systems RA II: Methods (FTA, ETA) Fault Tree Analysis (FTA) Problem description It is not possible to analyse complicated, highly-reliable or novel systems as black box
More informationSystem Reliability Analysis. CS6323 Networks and Systems
System Reliability Analysis CS6323 Networks and Systems Topics Combinatorial Models for reliability Topology-based (structured) methods for Series Systems Parallel Systems Reliability analysis for arbitrary
More informationMulti-State Availability Modeling in Practice
Multi-State Availability Modeling in Practice Kishor S. Trivedi, Dong Seong Kim, Xiaoyan Yin Depart ment of Electrical and Computer Engineering, Duke University, Durham, NC 27708 USA kst@ee.duke.edu, {dk76,
More informationProbability Midterm Exam 2:15-3:30 pm Thursday, 21 October 1999
Name: 2:15-3:30 pm Thursday, 21 October 1999 You may use a calculator and your own notes but may not consult your books or neighbors. Please show your work for partial credit, and circle your answers.
More informationMonte Carlo (MC) simulation for system reliability
Monte Carlo (MC) simulation for system reliability By Fares Innal RAMS Group Department of Production and Quality Engineering NTNU 2 2 Lecture plan 1. Motivation: MC simulation is a necessary method for
More informationUNIVERSITY 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 informationCHAPTER 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 informationDERIVATIVE FREE OUTPUT FEEDBACK ADAPTIVE CONTROL
DERIVATIVE FREE OUTPUT FEEDBACK ADAPTIVE CONTROL Tansel YUCELEN, * Kilsoo KIM, and Anthony J. CALISE Georgia Institute of Technology, Yucelen Atlanta, * GA 30332, USA * tansel@gatech.edu AIAA Guidance,
More informationMonte Carlo Simulation for Predicting the Reliability of a Control System for a Geothermal Plant. Corneliu Popescu, Daniela Elena Popescu
Proceedings World Geothermal Congress 5 Antalya, Turkey, 4-9 April 5 onte Carlo Simulation for Predicting the Reliability of a Control System for a Geothermal Plant Corneliu Popescu, Daniela Elena Popescu
More informationPractical 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 informationMonte Carlo Methods in High Energy Physics I
Helmholtz International Workshop -- CALC 2009, July 10--20, Dubna Monte Carlo Methods in High Energy Physics CALC2009 - July 20 10, Dubna 2 Contents 3 Introduction Simple definition: A Monte Carlo technique
More informationNon-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 informationIntegrated Dynamic Decision Analysis: a method for PSA in dynamic process system
Integrated Dynamic Decision Analysis: a method for PSA in dynamic process system M. Demichela & N. Piccinini Centro Studi su Sicurezza Affidabilità e Rischi, Dipartimento di Scienza dei Materiali e Ingegneria
More informationUNAVAILABILITY CALCULATIONS WITHIN THE LIMITS OF COMPUTER ACCURACY ABSTRACT
(Vol.2) 29, December UNAVAILABILITY CALCULATIONS WITHIN THE LIMITS OF COMPUTER ACCURACY R. Briš Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Ostrava, Czech Republic
More informationChapter 12. Spurious Operation and Spurious Trips
Chapter 12. Spurious Operation and Spurious Trips Mary Ann Lundteigen Marvin Rausand RAMS Group Department of Mechanical and Industrial Engineering NTNU (Version 0.1) Lundteigen& Rausand Chapter 12.Spurious
More informationChapter 5 Reliability of Systems
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
More informationSimulation. Version 1.1 c 2010, 2009, 2001 José Fernando Oliveira Maria Antónia Carravilla FEUP
Simulation Version 1.1 c 2010, 2009, 2001 José Fernando Oliveira Maria Antónia Carravilla FEUP Systems - why simulate? System any object or entity on which a particular study is run. Model representation
More informationAvailability and Maintainability. Piero Baraldi
Availability and Maintainability 1 Introduction: reliability and availability System tyes Non maintained systems: they cannot be reaired after a failure (a telecommunication satellite, a F1 engine, a vessel
More informationChapter 8. Calculation of PFD using Markov
Chapter 8. Calculation of PFD using Markov Mary Ann Lundteigen Marvin Rausand RAMS Group Department of Mechanical and Industrial Engineering NTNU (Version 0.1) Lundteigen& Rausand Chapter 8.Calculation
More informationReliability 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 informationMarkov Models for Reliability Modeling
Markov Models for Reliability Modeling Prof. Naga Kandasamy ECE Department, Drexel University, Philadelphia, PA 904 Many complex systems cannot be easily modeled in a combinatorial fashion. The corresponding
More informationCHAPTER 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 informationMIT Manufacturing Systems Analysis Lectures 6 9: Flow Lines
2.852 Manufacturing Systems Analysis 1/165 Copyright 2010 c Stanley B. Gershwin. MIT 2.852 Manufacturing Systems Analysis Lectures 6 9: Flow Lines Models That Can Be Analyzed Exactly Stanley B. Gershwin
More informationMonte Carlo Simulation. CWR 6536 Stochastic Subsurface Hydrology
Monte Carlo Simulation CWR 6536 Stochastic Subsurface Hydrology Steps in Monte Carlo Simulation Create input sample space with known distribution, e.g. ensemble of all possible combinations of v, D, q,
More informationReliability Analysis of a Single Machine Subsystem of a Cable Plant with Six Maintenance Categories
International Journal of Applied Engineering Research ISSN 973-4562 Volume 12, Number 8 (217) pp. 1752-1757 Reliability Analysis of a Single Machine Subsystem of a Cable Plant with Six Maintenance Categories
More informationDecision making and problem solving Lecture 1. Review of basic probability Monte Carlo simulation
Decision making and problem solving Lecture 1 Review of basic probability Monte Carlo simulation Why probabilities? Most decisions involve uncertainties Probability theory provides a rigorous framework
More informationChapter 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 information1.5. Applications. Theorem The solution of the exponential decay equation with N(0) = N 0 is N(t) = N 0 e kt.
6 Section Objective(s): The Radioactive Decay Equation Newton s Cooling Law Salt in a Water Tanks 151 Exponential Decay 15 Applications Definition 151 The exponential decay equation for N is N = k N, k
More informationMonte Carlo Simulation
Monte Carlo Simulation 198 Introduction Reliability indices of an actual physical system could be estimated by collecting data on the occurrence of failures and durations of repair. The Monte Carlo method
More informationStochastic Renewal Processes in Structural Reliability Analysis:
Stochastic Renewal Processes in Structural Reliability Analysis: An Overview of Models and Applications Professor and Industrial Research Chair Department of Civil and Environmental Engineering University
More informationRelationship between probability set function and random variable - 2 -
2.0 Random Variables A rat is selected at random from a cage and its sex is determined. The set of possible outcomes is female and male. Thus outcome space is S = {female, male} = {F, M}. If we let X be
More informationReliability 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 informationChapter 2 Random Variables
Stochastic Processes Chapter 2 Random Variables Prof. Jernan Juang Dept. of Engineering Science National Cheng Kung University Prof. Chun-Hung Liu Dept. of Electrical and Computer Eng. National Chiao Tung
More informationWe are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors
We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 3,900 116,000 120M Open access books available International authors and editors Downloads Our
More informationRecap of Basic Probability Theory
02407 Stochastic Processes Recap of Basic Probability Theory Uffe Høgsbro Thygesen Informatics and Mathematical Modelling Technical University of Denmark 2800 Kgs. Lyngby Denmark Email: uht@imm.dtu.dk
More informationProbability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers Roy D. Yates and David J.
Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers Roy D. Yates and David J. Goodman Problem Solutions : Yates and Goodman,.6.3.7.7.8..9.6 3.. 3.. and 3..
More informationProbability Density Functions
Statistical Methods in Particle Physics / WS 13 Lecture II Probability Density Functions Niklaus Berger Physics Institute, University of Heidelberg Recap of Lecture I: Kolmogorov Axioms Ingredients: Set
More informationRecap of Basic Probability Theory
02407 Stochastic Processes? Recap of Basic Probability Theory Uffe Høgsbro Thygesen Informatics and Mathematical Modelling Technical University of Denmark 2800 Kgs. Lyngby Denmark Email: uht@imm.dtu.dk
More informationAdvanced Methods for Fault Detection
Advanced Methods for Fault Detection Piero Baraldi Agip KCO Introduction Piping and long to eploration distance pipelines activities Piero Baraldi Maintenance Intervention Approaches & PHM Maintenance
More informationChapter 4: Monte Carlo Methods. Paisan Nakmahachalasint
Chapter 4: Monte Carlo Methods Paisan Nakmahachalasint Introduction Monte Carlo Methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo
More information0utline. 1. Tools from Operations Research. 2. Applications
0utline 1. Tools from Operations Research Little s Law (average values) Unreliable Machine(s) (operation dependent) Buffers (zero buffers & infinite buffers) M/M/1 Queue (effects of variation) 2. Applications
More informationReliability of Safety-Critical Systems Chapter 8. Probability of Failure on Demand using IEC formulas
Reliability of Safety-Critical Systems Chapter 8. Probability of Failure on Demand using IEC 61508 formulas Mary Ann Lundteigen and Marvin Rausand mary.a.lundteigen@ntnu.no &marvin.rausand@ntnu.no RAMS
More informationPaper 5 Naseri, M. Author contributions:
Paper 5 Naseri, M., Baraldi, P., Compare, M. and Zio, E., 216. Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions. Reliability Engineering & System
More informationStructural Reliability
Structural Reliability Thuong Van DANG May 28, 2018 1 / 41 2 / 41 Introduction to Structural Reliability Concept of Limit State and Reliability Review of Probability Theory First Order Second Moment Method
More informationSingle-part-type, multiple stage systems. Lecturer: Stanley B. Gershwin
Single-part-type, multiple stage systems Lecturer: Stanley B. Gershwin Flow Line... also known as a Production or Transfer Line. M 1 B 1 M 2 B 2 M 3 B 3 M 4 B 4 M 5 B 5 M 6 Machine Buffer Machines are
More informationValue of Information Analysis with Structural Reliability Methods
Accepted for publication in Structural Safety, special issue in the honor of Prof. Wilson Tang August 2013 Value of Information Analysis with Structural Reliability Methods Daniel Straub Engineering Risk
More informationSoftware 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 informationBasic notions of probability theory
Basic notions of probability theory Contents o Boolean Logic o Definitions of probability o Probability laws Why a Lecture on Probability? Lecture 1, Slide 22: Basic Definitions Definitions: experiment,
More informationBasic notions of probability theory
Basic notions of probability theory Contents o Boolean Logic o Definitions of probability o Probability laws Objectives of This Lecture What do we intend for probability in the context of RAM and risk
More informationProbability Methods in Civil Engineering Prof. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur
Probability Methods in Civil Engineering Prof. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur Lecture No. # 12 Probability Distribution of Continuous RVs (Contd.)
More informationLecture 4 - Random walk, ruin problems and random processes
Lecture 4 - Random walk, ruin problems and random processes Jan Bouda FI MU April 19, 2009 Jan Bouda (FI MU) Lecture 4 - Random walk, ruin problems and random processesapril 19, 2009 1 / 30 Part I Random
More informationDependable 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 informationEvaluating the PFD of Safety Instrumented Systems with Partial Stroke Testing
Evaluating the PF of Safety Instrumented Systems with Partial Stroke Testing Luiz Fernando Oliveira Vice-President NV Energy Solutions South America How did I get to writing this paper? Started doing SIL
More informationChapter 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 informationReliability of Safety-Critical Systems Chapter 9. Average frequency of dangerous failures
Reliability of Safety-Critical Systems Chapter 9. Average frequency of dangerous failures Mary Ann Lundteigen and Marvin Rausand mary.a.lundteigen@ntnu.no &marvin.rausand@ntnu.no RAMS Group Department
More informationQuantitative Reliability Analysis
Quantitative Reliability Analysis Moosung Jae May 4, 2015 System Reliability Analysis System reliability analysis is conducted in terms of probabilities The probabilities of events can be modelled as logical
More informationStochastic Analysis of a Two-Unit Cold Standby System with Arbitrary Distributions for Life, Repair and Waiting Times
International Journal of Performability Engineering Vol. 11, No. 3, May 2015, pp. 293-299. RAMS Consultants Printed in India Stochastic Analysis of a Two-Unit Cold Standby System with Arbitrary Distributions
More informationSTAT 2507 H Assignment # 2 (Chapters 4, 5, and 6) Due: Monday, March 2, 2015, in class
STAT 2507 H Assignment # 2 (Chapters 4, 5, and 6) Due: Monday, March 2, 2015, in class Last Name First Name - Student # LAB Section - Note: Use spaces left to answer all questions. The total of marks for
More informationImportance of the Running-In Phase on the Life of Repairable Systems
Engineering, 214, 6, 78-83 Published Online February 214 (http://www.scirp.org/journal/eng) http://dx.doi.org/1.4236/eng.214.6211 Importance of the Running-In Phase on the Life of Repairable Systems Salima
More informationFleet Maintenance Simulation With Insufficient Data
Fleet Maintenance Simulation With Insufficient Data Zissimos P. Mourelatos Mechanical Engineering Department Oakland University mourelat@oakland.edu Ground Robotics Reliability Center (GRRC) Seminar 17
More information1. 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 informationFailure 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 informationDiscrete Probability Distributions
Discrete Probability Distributions Chapter 06 McGraw-Hill/Irwin Copyright 2013 by The McGraw-Hill Companies, Inc. All rights reserved. LEARNING OBJECTIVES LO 6-1 Identify the characteristics of a probability
More informationBernoulli Counting Process with p=0.1
Stat 28 October 29, 21 Today: More Ch 7 (Sections 7.4 and part of 7.) Midterm will cover Ch 7 to section 7.4 Review session will be Nov. Exercises to try (answers in book): 7.1-, 7.2-3, 7.3-3, 7.4-7 Where
More informationThe Markov Decision Process (MDP) model
Decision Making in Robots and Autonomous Agents The Markov Decision Process (MDP) model Subramanian Ramamoorthy School of Informatics 25 January, 2013 In the MAB Model We were in a single casino and the
More informationCombinational 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 informationA techno-economic probabilistic approach to superheater lifetime determination
Journal of Energy in Southern Africa 17(1): 66 71 DOI: http://dx.doi.org/10.17159/2413-3051/2006/v17i1a3295 A techno-economic probabilistic approach to superheater lifetime determination M Jaeger Rafael
More informationECE302 Spring 2006 Practice Final Exam Solution May 4, Name: Score: /100
ECE302 Spring 2006 Practice Final Exam Solution May 4, 2006 1 Name: Score: /100 You must show ALL of your work for full credit. This exam is open-book. Calculators may NOT be used. 1. As a function of
More informationChapter 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 informationME 595M: Monte Carlo Simulation
ME 595M: Monte Carlo Simulation T.S. Fisher Purdue University 1 Monte Carlo Simulation BTE solutions are often difficult to obtain Closure issues force the use of BTE moments Inherent approximations Ballistic
More information