How Ot Predict Service Based On A Known Mtbf
Let's say we have a product that most often fails for one major component. Allow's say a fan (it could be anything, and while I don't accept annihilation against fans, it's piece of cake to picture).
Ok, this fan has a data sheet with the archetype reliability claim of 50,000 hours MTBF. For those that know nigh my disdain for MTBF (www.nomtbf.com) rest assured I'1000 non going to get into information technology hither. The basic approach for estimating the number of failure during any period of time does require a few pieces of information. MTBF is common on data sheets, so, in this case, that's where nosotros start.
Without whatever other information about the life distribution and given only MTBF, nosotros will have to use the exponential distribution. The cumulative distribution function is
$$ \big\displaystyle F\left( t \right)=1-{{eastward}^{-{}^{t}\!\!\diagup\!\!{}_{\theta }\;}}$$
where, F(t) is the probability of failure up till fourth dimension, t. Theta, θ, is the MTBF.
The adjacent piece of information we need is the warranty period or the catamenia of time of interest. In this case, let's say it's three years. And, since the fan is the primary business in this simple example, we tin can consider the duty cycle of the fan within the product. The sake of ease in this case, permit'southward say the fan in working full time (maybe a server production, for example). That means the fan will operate for 365 days x 24 hours ten 3 years = 26,280 hours.
Now we're ready to do the calculation.
t = 26, 280 hours
θ = 50,000 hours
Using the equation above, nosotros find 0.41, or we would expect that about 41% of the fans would neglect by three years. The time is related to the age of the private units, not production time. In curt, a lot would fail. How many?
We need how many units are shipped or expected to ship. Let's say, we are assuming we will produce ten,250 of these products, how many will come dorsum under warranty due to fan failure?
10,250 10 0.41 = 4202.5 or just over 4,000 fan failures.
Multiply the number of warranty failures by the toll of a warranty return to discover a number of warranty reserves to gear up bated.
If y'all accept whatsoever questions or would like to see other examples, please leave a annotate.
Related:
Confidence Intervals for MTBF (article)
Using The Exponential Distribution Reliability Function (commodity)
Reliability Goal (article)
How Ot Predict Service Based On A Known Mtbf,
Source: https://accendoreliability.com/how-to-calculate-warranty-failures/
Posted by: crusedowasobod.blogspot.com
0 Response to "How Ot Predict Service Based On A Known Mtbf"
Post a Comment