SOLUTIONS
, RELIABILITYENGINEERING –ALIFECYCLEAPPROACH t, t, t, t, t, t,
INSTRUCTOR’S t , MANUAL
CHAPTER 1 t ,
The Monty Hall Problem
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The truth is that one increases one’s probability of winning by changing one’s choice. The
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easiest way to look at this from a probability point of view is to say that originally there is a
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probability of ⅓ over every door. So there is a probability of ⅓ over the door originally chosen, and a
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combined probability of ⅔ over the remaining two doors. Once one of those two doors is
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opened, there remains a probability of ⅓ over the door originally chosen, and the other
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unopened door now has the probability ⅔. Hence it increases one’s probability of winning the
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car by changing one’s choice of door.
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This does not mean that the car is not behind the door originally chosen, only that if one were to
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repeat the exercise say 100 times, then the car would be behind the first door chosen about 33
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times and behind the alternative choice about 66 times. Prove for yourself using Excel!
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Another way to prove this result is to use Bayes Theorem, which the reader can source for
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himself on the internet.
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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
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failure and therefore without the need for maintenance. It is the Gamma value for the
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distribution. From the list of failure times 150, 190, 220, 275, 300, 350, 425, 475, the Offset is
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calculated as 97.42 hours – say 100 hours. This is the time for which there should be no
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probability of failure. It will be seen from the graph in the software with Beta = 2 that the
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distribution is of almost perfect normal shape and that the distribution does not begin at the
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origin. The gap is the 100 hours that the software calculates when asked.
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When the graph is studied for Beta = 2 it will be seen that there is a downward trajectory in the three
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left hand points. If this trajectory is taken down to the horizontal axis it is seen to intersect it at about
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120 hours. This is the estimation of Gamma. In the days before software this was always the
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most unreliable estimate of a Weibull parameter and the most difficult to obtain graphically.
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Assignment 1.3 t ,
When the offset is calculated it is seen to be negative at – 185.59 (say 180). This indicates that the
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distribution starts before zero on the horizontal axis. This is the phenomenon of shelf life. Some
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items have failed before being put into service. This can apply in practice to rubber
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components and paints, for example.
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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
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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
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R400k = 92%
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For DESIGN B we get from the WEIBULL-DR software (not shown here)
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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
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126922 cycles. For an average use of 8000 cycles per year we get 126922/8000 = 15.86 years A
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conservative guarantee would therefore by 15 years.
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NOTE: The above calculations ignore the γ value. If this is calculated, the following figures
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emerge as shown in Fig 1.4.2 (the obscuration of some of the figures is the way the current
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version of the software prints out)
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DESIGN A t,
β=3 t, t,
γ = 101 828.6 say 100 000
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For F = 1% at 90% confidence, F = 176149
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Dividing by 8000 we get 176149/8000 = 22 years
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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
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would have to be invoked – there are matters to consider in the determination of guarantees
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other than the test data provided. These matters could include corrosion, user abuse etc. Such
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factors are more likely to occur, the longer the operating period. Questions need to be asked
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such as is there an industry standard for such guarantees, what are competitors offering as
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guarantees, etc.
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A further point to note is that DESIGN B exhibits very peculiar characteristics if the γ value is
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taken into account. The β value remains at 2 but the γ value is negative at over 50 000 cycles!
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This implies that there is a probability of failure before entering service. This data looks suspect
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and further tests should be done to confirm the reliability characteristics of DESIGN B.
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