Your Reliability Engineering Professional Development Site
Prep notes for ASQ Certified Reliability Engineer exam ISSN 2165-8633
The CRE Preparation Notes series provides you with short practical tutorials on all the elements that make up the ASQ CRE body of knowledge. The articles provide introductory material, basics, how-tos, examples, and practical use guidance for the full range of reliability engineering concepts, terms, tools, and practices.
Keep your knowledge fresh by regularly reviewing topics and tools that make up reliability engineering.
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You will find the most recent tutorials in reverse chronological order below.
Reliability testing and data collection is a messy business.
We rarely receive perfect data where all units involved have a precise time to failure record. Sometimes we do not the precise time to failure information or some of the units are still operating.
The data is censored.
There are a few common types of censoring, each of which has statistical techniques to appropriately account for the unknown elements. [Read more…]
It is possible to use the circuit design or the circuit itself to evaluate electronics. This ranges from checking if the system is wired correctly (the right parts are present and attached properly) to a complex evaluation including the proper functioning of firmware and attached software.
Beyond visual inspection, we need a way to evaluate the microscopic structures with today’s electronic components.
The ability to provide inputs and measure outputs provides one view and in most cases is nondestructive. [Read more…]
There are times when we need to evaluate a product to determine is it assembled correctly or when looking for clues concerning a failure mechanism. Simple visual inspection may be sufficient and there are times when we need more information or detail.
A great first step is the use of an appropriate nondestructive evaluation (NDE) method.
Done correctly, we keep the item unchanged and available for shipment or further inspection.
The first time I changed the oil filter of my car (first car in high school) I smashed my knuckles against a grimy block of metal. More than once. It may have been my lack of experience or improper tools. Or, it may have been a rather poor design.
Watching the Indy 500, especially the pit stops, I quickly realized there has to be something different about those cars that lets them change tires, add oil & fuel, and clean the windshield in less time than it took me to get under the hood and find the oil filter. The cars were designed differently and that is what permitted the difference in service time.
In order to increase availability and minimize the cost of maintenance, we have to deliberately design the system to accommodate the needs of maintenance. [Read more…]
Reliability testing to determine what will fail or when will failures occur is expensive.
Organizations invest in the development of a product and attempt through the design process to create a product that is reliable. [Read more…]
Beyond understanding what will fail (failure mechanisms) we need to know when a system will fail. Both kinds of information can be found in a variety of sources.
Unlike the stock market where we are regularly warned that past performance does not indicate future performance; in reliability engineering past performance is the best predictor of future performance. [Read more…]
The other day a student said they are asking for failure dates, not failure rates. That is a marked improvement when requesting information for the many sources of reliability information. It is not sufficient though.
Also ask for the type of failures expected or encountered during the testing or observation. [Read more…]
We rely on data to make decisions, to reveal patterns or trends, to learn about our systems and world. Data has many forms and sources. Reliability data may provide what will fail and/or when a device will fail. [Read more…]
Here’s an overview of the non-parametric test to evaluate if a set of samples have the same variance. If the variances are equal they have homogeneity of variances.
Some statistical tests assume equal variances across samples, such as analysis of variance and many types of hypothesis tests. It is also assumed for statistical process control purposes to determine stability (often done with range (r chart) or standard deviation (s charts). [Read more…]
A contingency table, as in the chi-squared test of independence, reveals if two sets of data or groups are independent or not. It does not reveal the strength of the dependence. The contingency coefficient is a non-parametric measure of the association for cross-classification data. [Read more…]
The chi-square ( $- \chi^2 -$) test provides a means to determine independence between two or more variables. In this case, it works for count data.
Contingency table or row and column (r x c) analysis are other common names for this analysis. It is useful when comparing results from different treatments or processes. [Read more…]
Here’s an overview of a distribution-free approach commonly called the Kaplan-Meier (K-M) Product Limit Reliability Estimator.
There are no assumptions about underlying distributions. And, K-M works with datasets with or without censored data. We do need to know when failures or losses (items removed from the evaluation or test other than as a failure. Censored items). [Read more…]
Just opened registration for a CRE preparation course. http://fmsrel.com/1hW57Yt
Starting in August and running through September. All online.
Recently I participated in an ASQ CRE focus group with a few of our peers. The ASQ facilitator prompted each of use to answer a few questions. Then she asked each of us to contribute to a “Start, Stop, Change” exercise focused on the CRE exam.
It appears ASQ is starting another round of reviews and possible updates to the CRE Body of Knowledge and exam process. That is good. [Read more…]
This is a non-parametric test to compare ranked data from three or more groups or treatments. The basic idea is to compare the mean value of the rank values and test if the samples could are from the same distribution or if at least one is not.
The null hypothesis is the data from each group would receive about the same mean rank score. We are comparing rank values, not the actual values. [Read more…]
