Average Length of Life of Two Exponentially Distributed Components

In summary, the conversation discusses the probability density function for the average length of life of two independent electronic components in a missile guidance system, with each component following an exponential distribution with mean 1. The resulting density function is f(u) = 4ue^(-2u), which has a gamma distribution with α=2 and β=1/2. The conversation also includes a discussion on obtaining the average length of life and clarifying the use of "average" in the question.
  • #1
kjartan
15
0
1. Suppose that two electronic components in the guidance system for a missile operate independently and that each has a length of life governed by the exponential distribution with mean 1 (with measurements in hundreds of hours).

(a) Find the probability density function for the average length of life of the two components.

(b) Find the mean and variance.

2. f(y) = e^(-y) for y>=0
For (a), I obtained, f(u) = u*exp(-u) for u>=0. Still an exponential distribution.
The back of the text had f(u) = 4u*exp(-2u), where f has a gamma distribution with α=2, β=1/2.


Thanks for any and all help!
 
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  • #2


How'd you come up with your result of f(u) = ue-u?
 
  • #3


Thanks for taking a look!
What I did was this.

f(y1) = e^(-y1), and f(y2) = e^(-y2), since the general form for an exponentially distributed random variable is f(y) = (1/[tex]\beta[/tex])*e^(-y/[tex]\beta[/tex]). Where the mean is beta, and we are given that the mean is 1.

Then, since y1 & y2 are independent, f(y1,y2) = f(y1)*f(y2).

Thus, e^(-y1)*e^(-y2) = e^(-(y1+y2)).

Then, the distribution function, P(Y1+Y2<u) is given by
[tex]\int^{u}_{0}[/tex][tex]\int^{u-y2}_{0}e^(-(y1+y2))[/tex]dy1dy2 = 1-ue^(-u)-e^(-u)

Then, to get the density function, we differentiate with respect to u, obtaining:
f(u) = ue^(-u)
 
  • #4


I think you just need to calculate the average length of life as (y1+y2)/2, not simply (y1+y2).
 
  • #5


Ok, almost there . . .

So, since we have (y1+y2)/2, we switch the upper limits of integration as such:
[tex]\int^{2u}_{0}[/tex][tex]\int^{2u-y2}_{0}e^(-(y1+y2)/2)dy1dy2[/tex]= 4(1-e^(-u)-ue^(-u)).

Then take d/du, obtaining:
4ue^(-u)I must be tired, I'll have to take another look at this to figure out what I'm doing wrong . . . need to obtain 4ue^(-2u) . . .

Thanks! I think that's the crux of the problem that I was missing, now I just need to fill in the last detail . . . I appreciate your help.
 
  • #6


The joint probability density doesn't change; only the limits do.
 
  • #7


Whoops! Thanks!

Ok, now finally . . .

integrating e^(-(y1+y2)) with our new limits of integration and then taking d/du, to obtain

f(u)=4ue^(-2u)

Presto!

Thanks! Ok, so I should have paid more attention to their use of "average" in the question, I kind of read over that.
I appreciate your help, I stared and stared at that problem and could not figure out what I had done wrong.
 

What does "exponentially distributed" mean?

Exponentially distributed refers to a probability distribution where the probability of an event occurring decreases exponentially as the time between events increases. In other words, the longer the time between events, the less likely they are to occur.

How is the average length of life calculated for exponentially distributed components?

The average length of life is calculated by taking the inverse of the failure rate parameter, often denoted as λ (lambda). This is also known as the mean time between failures (MTBF).

What factors can affect the average length of life for exponentially distributed components?

The main factor that affects the average length of life is the failure rate parameter, λ. This can be affected by various factors such as the quality of the components, environmental conditions, and maintenance practices. Other factors that can influence the average length of life include the type of distribution, the sample size, and the time period being studied.

How is the average length of life used in reliability engineering?

The average length of life is an important measure in reliability engineering as it allows for the prediction and analysis of the failure rate of components. It can be used to determine the expected lifetime of a system, plan maintenance and replacement schedules, and identify potential areas for improvement in the design or manufacturing process.

What are some limitations of using the average length of life for exponentially distributed components?

One limitation is that the average length of life is based on a statistical average and may not accurately represent the actual lifespan of individual components. Additionally, it assumes that failures occur randomly and independently, which may not always be the case. Other limitations include not accounting for wear and tear, changes in environmental conditions, and the potential for sudden catastrophic failures.

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