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Prep notes for ASQ Certified Reliability Engineer exam ISSN 2165-8633
The idea of the CRE Preparation Notes series is to provide you short practical tutorials on all the elements that make up the ASQ CRE body of knowledge. The articles provide introductionary material, basics, how-to’s, examples, and practical use guidance for the full range of reliability engineering concepts, terms, tools, and practices.
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You will find the most recent tutorials in reverse chronological order below. Below each article is the section and specific clause of the CRE Body of Knowledge that tutorial addresses. Click on those tags to find other articles on the same topic. To the right on the sidebar, there is a listing of the 7 major categories in the body of knowledge - it's a quick way to find groups of articles on each specific area. You can also use the search function to locate articles, podcasts, or tutorials on specific topics.
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…]
This week in our CRE Test Prep class/webinar we covered the Advance Statistics section of the CRE Primer, and I felt great that we stayed an hour more going over these though topics.
One of the topics was Hypothesis Testing.
Let me share some of the questions that arose during that section. [Read more…]
A guest post by Andrew O’Connor, of Relken Engineering Pty Ltd
Common Cause Failures (CCF) is one of the reasons why a classical reliability model of your system may dangerously underestimate the risk of failure. It directly attacks the benefits of providing redundancy by creating a single point of failure. In fact, studies have shown that CCF events may contribute between 20% – 80% of the unavailability of safety systems within nuclear reactors [Werner 1994]. This post will “Describe this type of failure (also known as common cause mode failure) and how it affects design for reliability. (Understand)” [CRE BOK III.A.4] [Read more…]
Last week during our CRE Test Prep class, we were covering the Basic Statistics section in the CRE Primer and had several questions regarding Confidence Limits for Reliability.
All of them were fair questions, and when students are asking these types of questions, the class gets better… [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…]
In a recent LinkedIn post a fellow Reliability Engineer is asking the Reliability Engineering community in how to prepare for the CRE exam. Rapidly everyone is recommending books, study groups, and training courses. My recommendation to anyone taking the CRE exam is to be very familiar with your approved ASQ calculator. Here is a link that I found in [Read more…]
EAM & CMMS Systems, 10 times more data in the system or 10 time less done with the data available?.
A nice short article about the problem of data, data, too much data.