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Solution Manual to Reliability Engineering 2nd Edition - Bradley (2020)

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This comprehensive instructor's manual provides detailed solutions, case study analyses, and assignment guidance for all chapters of "Reliability Engineering" by Bradley, covering fundamental concepts in reliability theory, failure analysis, and practical maintenance management. The manual addresses key topics including Weibull analysis with detailed calculations of beta values, correlation coefficients, and failure probabilities at specified confidence levels for real-world datasets such as spring design comparisons, failure-free operating period determination, and hyper-exponential distribution identification from limited failure data. It extensively covers FMECA (Failure Mode Effects and Criticality Analysis) with severity and occurrence probability scales, fault tree analysis with probability calculations for basement flooding scenarios including battery drainage, pump failures, and system reliability improvements through component upgrades. The manual provides comprehensive treatment of reliability block diagrams (RBD) with parallel and series system calculations, availability predictions using Weibull parameter estimation for MTBF, and effective availability adjustments for storage capacity considerations. Case study analyses include detailed examinations of the Challenger disaster with O-ring failure statistics, Piper Alpha with communication failures and lockout/tagout issues, Flixborough with free body diagram calculations for pipe stress, Hyatt Regency with load distribution analysis, Chernobyl with human factors considerations, and Embraer aircraft crash with maintenance procedure breakdowns. Maintenance management topics include RCM (Reliability Centered Maintenance) implementation challenges, TPM (Total Productive Maintenance) case studies, hangar queen aircraft practices, Laplace trend analysis for maintained systems, and root cause failure analysis (RCFA) methodologies. Financial analysis tools covered include IRR, NPV, payback period calculations in Excel, linear programming optimization with Solver for production mix decisions, Monte Carlo simulation for stock control and loading bay optimization, and economic evaluation of maintenance strategies. Human factors and organizational behavior are addressed through readings on maintenance error prevention, management support for reliability initiatives, and the critical distinction between technical and programmatic solutions to reliability problems, making this manual an essential resource for engineering instructors, reliability practitioners, and students seeking to understand both the theoretical foundations and practical applications of reliability engineering in industrial settings.

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Reliability Engineering
Course
Reliability Engineering

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All Chapters Covered




SOLUTIONS

, RELIABILITYENGINEERING –ALIFECYCLEAPPROACH t, t, t, t, t, t,




INSTRUCTOR’S t , MANUAL
CHAPTER 1 t ,




The Monty Hall Problem
t, t, t,




The truth is that one increases one’s probability of winning by changing one’s choice. The
t, t, t, t, t, t, t, t, t, t, t, t, t, t,



easiest way to look at this from a probability point of view is to say that originally there is a
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




probability of ⅓ over every door. So there is a probability of ⅓ over the door originally chosen, and a
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



combined probability of ⅔ over the remaining two doors. Once one of those two doors is
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




opened, there remains a probability of ⅓ over the door originally chosen, and the other
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



unopened door now has the probability ⅔. Hence it increases one’s probability of winning the
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



car by changing one’s choice of door.
t, t, t, t, t, t, t,




This does not mean that the car is not behind the door originally chosen, only that if one were to
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




repeat the exercise say 100 times, then the car would be behind the first door chosen about 33
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




times and behind the alternative choice about 66 times. Prove for yourself using Excel!
t, t, t, t, t, t, t, t, t, t, t, t, t, t,




Another way to prove this result is to use Bayes Theorem, which the reader can source for
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




himself on the internet.
t, t, t, t,




Assignment 1.2: Failure Free Operating Period t , t , t , t , t ,




The FFOP (Failure Free Operating Period) is the time for which the device will run without
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




failure and therefore without the need for maintenance. It is the Gamma value for the
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



distribution. From the list of failure times 150, 190, 220, 275, 300, 350, 425, 475, the Offset is
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




calculated as 97.42 hours – say 100 hours. This is the time for which there should be no
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



probability of failure. It will be seen from the graph in the software with Beta = 2 that the
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



distribution is of almost perfect normal shape and that the distribution does not begin at the
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




origin. The gap is the 100 hours that the software calculates when asked.
t, t, t, t, t, t, t, t, t, t, t, t, t,




When the graph is studied for Beta = 2 it will be seen that there is a downward trajectory in the three
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




left hand points. If this trajectory is taken down to the horizontal axis it is seen to intersect it at about
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



120 hours. This is the estimation of Gamma. In the days before software this was always the
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




most unreliable estimate of a Weibull parameter and the most difficult to obtain graphically.
t, t, t, t, t, t, t, t, t, t, t, t, t, t,




Assignment 1.3 t ,




When the offset is calculated it is seen to be negative at – 185.59 (say 180). This indicates that the
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




distribution starts before zero on the horizontal axis. This is the phenomenon of shelf life. Some
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




items have failed before being put into service. This can apply in practice to rubber
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




components and paints, for example.
t, t, t, t, t,




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,Assignment 1.4: The Choice between Two Designs of Spring t, t, t, t, t, t, t, t,




DESIGN A t , DESIGN B t,




Number Cycles to Failure t, t, Number Cycles to Failure t, t,




1 726044 1 529082

2 615432 2 729000

3 807863 3 650000

4 755000 4 445834

5 508000 5 343280

6 848953 6 959900

7 384558 7 730049

8 666600 8 973224

9 555201 9 258006

10 483337 10 730008



Using the WEIBULL-DR software for DESIGN A above we get
t, t, t, t, t, t, t, t, t,



β=4 t, t,



Correlation = 0.9943 t, t,




F400k = 8% (measured from the graph in the Weibull printout below Fig M1.4 Set A) Hence
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



R400k = 92%
t, t, t,




For DESIGN B we get from the WEIBULL-DR software (not shown here)
t, t, t, t, t, t, t, t, t, t, t,



Β=2 t, t,



Correlation = 0.9867 t, t,




F400k = 20% t, t,



Hence R400k = 80% t, t, t,




Hence DESIGN A is better t, t, t, t,




From Fig 1.4.1 Set A we can read in the table that for F = 1% at 90% confidence, the R value is
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



126922 cycles. For an average use of 8000 cycles per year we get 126922/8000 = 15.86 years A
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t ,



conservative guarantee would therefore by 15 years.
t, t, t, t, t, t, t,




NOTE: The above calculations ignore the γ value. If this is calculated, the following figures
t, t, t, t, t, t, t, t, t, t, t, t, t, t,



emerge as shown in Fig 1.4.2 (the obscuration of some of the figures is the way the current
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



version of the software prints out)
t, t, t, t, t, t,




DESIGN A t,




β=3 t, t,



γ = 101 828.6 say 100 000
t, t, t, t, t, t,



For F = 1% at 90% confidence, F = 176149
t, t, t, t, t, t, t, t, t,



Dividing by 8000 we get 176149/8000 = 22 years
t, t, t, t, t, t, t, t, t,




2
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, Fig 1.4.1 Set A t, t, t,




A figure of 22 years or even 15 years for any guarantee is very long indeed. Company policy
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



would have to be invoked – there are matters to consider in the determination of guarantees
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




other than the test data provided. These matters could include corrosion, user abuse etc. Such
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



factors are more likely to occur, the longer the operating period. Questions need to be asked
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



such as is there an industry standard for such guarantees, what are competitors offering as
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




guarantees, etc.
t, t,




A further point to note is that DESIGN B exhibits very peculiar characteristics if the γ value is
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



taken into account. The β value remains at 2 but the γ value is negative at over 50 000 cycles!
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,




This implies that there is a probability of failure before entering service. This data looks suspect
t, t, t, t, t, t, t, t, t, t, t, t, t, t, t, t,



and further tests should be done to confirm the reliability characteristics of DESIGN B.
t, t, t, t, t, t, t, t, t, t, t, t, t, t,




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Uploaded on
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