Your Reliability Engineering Professional Development Site
Let’s work a sample size problem.
A random sample size, n, is to be taken from a large population having a standard deviation of 1″. The sample size is to be determined so that there well be a 0.05 risk probability of exceeding a 0.1″ tolerance error in using the sample mean to estimate μ. Which of the following values is nearest the required sample size?
a. 42
b. 106
c. 203
d. 384 [Read more…]
I recently received a question concerning what sample size to use when assessing call center calls. Not a lot of information in the request, so my answer was rather general. And, thought it might provide some insight to others facing sample size questions of their own. [Read more…]
note: if you know of a great online resource that isn’t on this list, please add a comments and we’ll add it to the list for all to share.
Shon Isenhour recently posted on Linkedin a question about online resources helpful for reliability engineers. I added my list and hadn’t seen any other links added. There must be more than the few I know about – what can you add to this list? [Read more…]
Ran across an interesting graphic in a new book recently. It single-handedly placed dependability in its proper context. It is an umbrella term that includes most of what we commonly think of as reliability and the other ‘ilities.’ It encompasses the various connotations of dependable and reliable that are conveyed during common use. And, the term dependability permits the overarching context for defining very clearly the various [Read more…]
Two short questions to evaluate your knowledge of failure mechanisms (root causes) and common reliability models. The answers will be posted in a comment, later.
Which of the following failure root causes is most likely NOT due to power line variation (electronic-based product)?
A. Circuit design margin exceeded
B. Power dissipation
C. In-rush current response
D. Mechanical fatigue [Read more…]
Let’s say we have two identical pumps share a load in parallel. The failure rate for a pump in this mode of operation is 0.0002 failures per hour. If one pump has to carry the full load alone, that pumps failure rate increases to 0.0009 failures per hour.
What is the reliability of the two pump system over a 168 hour week of operation? [Read more…]
A foundational element of probability and statistics is counting. How many ways could something occur? A simple example is a pass or fail criteria, thus when evaluating a product there are two possible outcomes. [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…]
The other day I got a question about the difference between ALT and HALT. There was some confusion probably because of the similar words in the acronym. ALT is Accelerated Life Test, and HALT is Highly Accelerated Life Test. [Read more…]
Failure mode and effects analysis, or FMEA, is a tool for the identification and prioritization of possible ways a product or process can fail. The intent is to use that information to make improvements to the product or process.
I think of FMEA (and related processes like FMECA, dFMEA, etc.) as structured brainstorms that provide a means to focus on what’s important. [Read more…]
The reliability goal is a key element across the entire product lifecycle. From product definition to determining warranty to judging performance, knowing the goal in clear terms sets the stage for a successful product.
Reliability in engineering terms is the probability of satisfactory product performance within a defined environment over a stated duration. [Read more…]
There a few different ways to sample a lot (or group) of material to determine if it has an acceptably low failure rate (or proportion that are considered ‘bad’). The following is an example of the sequential sampling method, which happens to be rather efficient by generally using the fewest samples for the same risk protection. [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…]