Your Reliability Engineering Professional Development Site
Running through a couple of practice CRE exams recently (yeah, I know I should get out more…) found a few formulas kept coming up in the questions. While it is not a complete list of equation you’ll need for the exam, the following five will help in many of the questions. They seem popular maybe because the relate to key concepts in the body of knowledge, or they are easy to use in question creation. I do not know why. [Read more…]
Received a sample problem from someone preparing for the CRE exam saying it was a tricky one.
The hazard rate function for a device is given by
0.001 if t ≤ 10 hours and 0.01 if t > 10 hours
What is the reliability of this device at 12 hours?
I first draw the hazard function [Read more…]
Down to the last week of preparation for the exam on March 2nd. Good luck to all those signed up for that exam date. Time to focus on preparing your notes, organizing your references and doing a final run through of practice exams. [Read more…]
In those situations where we sample without replacement, meaning the odds change after each sample is drawn, we can use the hypergeometric distribution for modeling. Great, sounds like statistician talk. So, let’s consider a real situation. [Read more…]
Let’s say the results of software testing averaged three defects per 10,000 lines of code. The criteria for release is 90% probability of 5 or fewer defects per 10k lines.
If this product ready for release?
The Poisson distribution is appropriate here as it is useful for modeling defects per unit, count per area, or arrivals per hour. If the data, in this case, the defect count per lines of code to be modeled by the Poisson distribution, the probability of an occurrence (defect in this case) has to be proportional to the interval (lines of code in this case). Also, the number of occurrences (defects) per interval must be independent (more on statistical independence in another post). [Read more…]
The lognormal distribution has two parameters, μ, and σ. These are not the same as mean and standard deviation, which is the subject of another post, yet they do describe the distribution, including the reliability function. [Read more…]
First the Question:
Fred,
Early in the FMEA lecture you worked through a homework problem and you mentioned that a cdf may not be linear (hence the reason for giving three points in a reliability goal). Can you give an example of two of things you’ve seen with non-linear cdf’s? I’ve only done limited reliability testing at this point, but everything I’ve done and every example I’ve ever seen have had linear cdf’s.
Thanks,
John
And, my response: [Read more…]
Let’s say we have a product that most often fails for one major component. Let’s say a fan (it could be anything, and while I don’t have anything against fans, it’s easy to picture).
Ok, this fan has a data sheet with the classic reliability claim of 50,000 hours MTBF. For those that know about my disdain for MTBF (www.nomtbf.com) rest assured I’m not going to get into it here. The basic approach for estimating the number of failure during any [Read more…]
There are four functions related to life distributions of importance to reliability engineering.
Nearly all textbooks on reliability either introduce or use these functions. Likewise, nearly every calculation related to reliability statistics also uses at least one of functions.
Suppose we produced 100 units of a product and tracked them all till failure over time.
Eventually, all of them would fail. And, unlike the Parson’s One Hoss Shay the products and their components would not all fail at the same time.
One way to track the failures over time is to use a simple histogram.
By plotting the number of failures each month, for example, we would [Read more…]