# Subject ENGG961 Systems Reliability Engineering

Subject: ENGG961: Systems Reliability Engineering
Assignment 2: Reliability Data Modelling
Purpose:
To complete failure data modelling by applying the knowledge learnt.
Learning objectives:
1. Understand and apply reliability concepts and terminology.
2. Interpret the principles of reliability engineering and reliability engineering processes.
3. Identify and use mathematical tools and techniques commonly used in systems reliability analysis and how
they can be applied to different situations.
4. Collect and analyse reliability data (times to failure and times to repair) using empirical and parametric
methods (exponential, Weibull, normal and lognormal are in syllabus); conduct Goodness- of-fit test for
model selection.
1. Generator is a critical component in power drive-train system in a wind turbine. The generators must pass
the required reliability testing before they can be applied to wind turbines. Three suppliers are providing
generators for wind turbine manufacturers. Extensive reliability testing has resulted in the determination of
the failure distribution for each vendor’s generator, see below:
Vendor Failure Distribution
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Wind Power Generator Weibull distribution with scale parameter ? = 6,200 operating days and shape
parameter ? = 1.20.
GE Generator Lognormal distribution with median time tmed = 4,000 operating days and s = 0.65 (s is shape
parameter, see Page 81 of the prescribed textbook)
Siemens Generator F(t) = 1 ? a2/(a+t) 2 where 0 ? t (measured in operating days, a = 6,500)
Compare each vendor’s product by finding (total 20 marks)
1. R(10 years) (1 year =330 days) (2 marks)
2. The MTTF and median time to life (2 marks)
3. The mode of each distribution model by plotting pdf (5 marks)
4. The 95-percent design life (2 marks)
5. The reliability for the next 5 years if it has survived the first 10 years (4 marks)
6. Plotting the hazard function (3 marks)
7. Whether the hazard function is DFR, CFR, or IFR (2 marks)
Your report on the questions is required to present in the following template table:
No. Item Wind Power Generator GE Generator Siemens Generator
1 R(10 years)
2 MTTF =
tmed =
3 tmod determined by plotting
4 t0.95
5 Reliability in next 5 years
6 Hazard function plotting
7 Hazard function type
2. Fifty automobiles using a new type of motor oil were monitored over a period of several months to
determine when the oil is needed to be replaced due to the level of contaminants. These times were recorded
in kilometres. Several units were censored from the study as a result of vehicle losses. Motor-oil failures are
believed to follow a Weibull distribution.
1770 2034 2876 3200+ 2390 5751 553 1450 2319 682
2220 2207+ 654+ 1855 1393 480 1526 4006 3069 2100
1230 5050+ 2019 2622 3675 1714 810+ 2146 1819 1793
1187 2300 2859 2038 2180 2330 2110 2550 1980 890
1500 2750 2450 1110 1220+ 1250 4000 3150 850 3200
+ means censored in testing
Answer the following questions (total 24 marks)
1. Give expression of Weibull model parameter estimate using maximum likelihood approach and determine
a replacement interval in kilometres based upon a 95-percent design life. Compare this to the mean time to
failure (MTTF) and median time to failure. (8 marks)
2. Use Least Squares estimate to find the model parameters and give R2 value of the distribution model
obtained for the given data set. (8 marks)
3. Plot the data points and the fitted model plots using maximum likelihood estimates and the Least Square
estimate on the Weibull Probability Paper (WPP). (8 marks)
3. A company manufactures various household products. Of concern to the company is its relatively low
production rate on its powdered detergent production line because of the limited availability of the line itself.
The line fails frequently generating considerable downtime. The line has two primary failure modes: Mode A
reflects operation failures such as jams, breaks, spills, and overflows on the line and Mode B represents
mechanical and electrical failures of motors, glue guns, rollers, belts, etc. Over the last 65 line start-ups, the
following times in hours until the line shut down were recorded:
Time mode 0.1
A 0.3
A 0.3
A 0.4
A 0.5
A 0.6
A 0.8
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A 1.1
A 1.1
A 1.2
A 1.5
A 1.8
A 1.9
A
Time mode 2.1
A 2.3
A 2.8
A 4.0
A 5.7
A 8.7
A 9.4
A 10.0
A 10.1
A 11.7
A 13.7
A 15.1
A 15.3
A
Time mode 19.1
A 19.3
A 19.6
B 21.3
A 23.2
A 24.9
A 25.2
A 32.7
A 34.4
B 47.3
A 59.9
B 64.8
A 65.5
A
Time mode 73.1
B 86.8
B 93.0
B 99.0
B 103.1
B 115.5
B 118.3
B 118.8
B 122.7
B 125.0
B 134.4
B 135.4
B 147.6
B
Time 149.1 160.4 161.5 163.4 165.4 180.5 181.1 192.6 196.2 199.6 204.1 205.2 211.4
mode B B B B B B B B B B B B B
Answer the following questions (total 40 marks)
1) From among the Weibull and lognormal distributions find the best fit for each failure mode based
upon the Least Squares R2 value. (10 marks)
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2) Find a best fit model from among Weibull, gamma and lognormal distributions by comparison of AICc
and BIC values calculated for each of the three distribution models selected for Mode A and
Mode B failure. (10 marks)
3) Use maximum likelihood estimates (MLE) to compute the reliability that the line will operate for one hour
(a) without a Mode A failure, and (b) without either Mode A or B failure. (5 marks)
4) Referring to the results obtained in 3), can you find a better fit model for the test data including Mode A
and Mode B failure? If not, explain why? (10 marks)
5) What conclusion can be reached concerning the operational failures? (5 marks)
4. Fifty units are placed on test with test terminated after 100 days (2400 hours). The following failure times
in hours were observed: (total 10 marks)
19 23 145 196 210 220 240 244 318 322 405 440 455 480 528
540 550 554 588 642 645 658 726 765 780 811 911 1059 1063 1115
1298 1313 1416 1531 1570 1750 1970 2058 2110 2285
1) From among Weibull and lognormal distributions find a better fit for the test data. (5 marks)
2) Test the selected model using Chi-Square Goodness-of-Fit test. (5 marks)
5. A reliability engineer is asked to answer the following questions. Can you help answer them? (total 6
marks)
1) If two distributions have the same MTTF, can it be concluded that the reliabilities given by the two
distributions are of the same? Give a short discussion. (3 marks) 2) Is reliability lower if failure rate is
higher? Give a short discussion. (3 marks)
Learning Guides
In order to complete the assignment tasks, you need to read Chapter 4, Chapter 12 and Chapter 15 given that
you have learnt Chapter 1 to Chapter 3; and you are required to have knowledge of Statistical Tests in
Chapter 16 in the prescribed textbook. While you are reading, you need to understand or answer the
following questions:
1) Be familiar with terminologies used in reliability engineering such as MTTF, median time to failure, CDF,
pdf, hazard function, failure rate, mode of a distribution, confidence interval, Quantiles, Least Square
estimate, maximum likelihood estimate (MLE), Goodness-of-Fit test.
2) Be familiar with distribution models such as exponential, normal, lognormal, Weibull, gamma distribution.
3) Know how to calculate reliability, failure rate, pdf.
4) Understand definitions of MTTF and MTBF, how to calculate each of them.
5) Understand why MTBF was popularly applied since World War II? Need to consider if it should be
utilised?
6) Is reliability lower if failure rate is higher?
7) How to calculate reliability if a distribution model is given?
8) Understand Type-I and Type-II tests and data characteristics obtained under each test.
9) Understand Least-Squares estimate of model parameters.
10) Understand maximum likelihood estimates of model parameters.
11) Get known of Weibull plot and Normal plot, i.e., plot data on Weibull probability paper (WPP) and
Normal probability paper.
12) How to select an optimal model for a test data set? To be familiar with Goodness-of-Fit test (Chapter 16).
13) Learn to know what are AIC, AICc and BIC, and how to use them.
(http://en.wikipedia.org/wiki/Akaike_information_criterion;
http://en.wikipedia.org/wiki/Bayesian_information_criterion )
14) Learn how to calculate 5% and 95% confidence level given a data point.
15) Learn to know how to calculate the lower and upper bound for a 95% confidence interval in parameter
estimate.
16) Learn to use and become very familiar with JMP and Excel