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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.
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You will find the most recent tutorials in reverse chronological order below.
Customers of your product would like the particular one they have to work. To work over time.
If a product meets the customer’s expectation by working as long or longer than they expected it to do so, then they may consider your product reliable.
We use reliability goals to discuss the customer’s reliability expectations. It is the establishment of requirements that converts the customer expectations to development and production obligations. [Read more…]
We are rather good at being surprised when setting expectations for the future. This is the essence of risk. The difference between what we expect to occur or would like to occur, and what does occur.
The definition of risk in ISO 9000:2015 and ISO 31000 include the phrase “ effect of uncertainty”. Let’s remove some of the uncertainty around the term uncertainty in the context of risk and risk management. [Read more…]
In a customer’s mind, the product works or it doesn’t. If it doesn’t work as expected it has failed. This may or may not be a reliability problem.
A customer or someone using your product brings a set of expectations to the experience. The range of expectations may range from very little to very high functioning, value production, and durability.
Failures are defined by customers.
In part, this is the functional capability, the operating within specifications, and the durability. The customer’s definition of reliability may or may not follow the design specifications.
It is the comparison of what should happen to what does happen. [Read more…]
When something doesn’t work as expected, it is a failure. A common response to a failure by an organization is to restore the system or remedy the situation.
Each failure is unique to the product, industry, customer situation, expectations, etc. Selecting the appropriate response or corrective action when confronted with a failure may or may not be obvious.
Selecting the right corrective action depends on the business and legal factors, along with customer expectations.
For a given failure, thinking through the range of possible responses and selecting the right one takes care to meet the various stakeholder’s requirements or expectations. [Read more…]
From the simplest to the most complex system, building and using a reliability model permits the entire team to make better decisions.
Understanding and monitoring system reliability involves knowing both:
We use system reliability models to identify weak links, and focus resources, to meet our desired reliability goals.
Being able to build the right model to meet your team’s needs best is one of your roles as a reliability professional. [Read more…]
The term variance is a statistical concept related to the spread or dispersion of a set of data. Second to the mean, it a common value we may calculate.
We find standard deviation easier to understand and use (it uses the same units as the data) whereas variance uses the units squared.
We use variance in quite a few different ways. Let’s review just a few. [Read more…]
The world of risk management has a unique set of risk terminology.
Your ability to incorporate reliability concerns into risk discussions hinges on understanding the terms in use.
Let’s explore a few terms and how they relate to reliability engineering. [Read more…]
Creating a reliable product that meets customer expectations is risky.
What is risk and how does one go about managing risk? The recent set of ISO standard updates elevates risk management.
A starting place is a definition. [Read more…]
Chapter 7 Design for Reliability of the book Practical Reliability Engineering starts with:
The reliability of a product is strongly influenced by decisions made during the design process.
The key message here is reliability occurs at the point of decision. Each time someone makes a decision, selects a component, chooses a material, assumes a use profile, the eventual product reliability takes shape.
Design for Reliability, DfR, is about making good decisions across the organization concerning reliability. [Read more…]
There are a few different reasons we explore differences in scale.
Keep in mind that the scale of a dataset is basically the spread of the data. For most datasets, we’re examining the variance.
Hypothesis tests comparing means vary depending on the assumption of equal variances. Thus testing that assumption requires methods to adequately test the homogeneity of variances. The F-test should come to mind as it is a common approach.
Some datasets do not lend themselves to using the F-test, which is applicable using real numbers. Some datasets gather information that is ordinal or interval data, thus we need another approach to test for differences in scale. [Read more…]
The Hartley test is an extension of the F distribution-based hypothesis test checking if two samples have different variances.
The F test works with two samples allowing us to compare two population variances based on the two samples. This test does not work for three or more populations. We could conduct multiple pairwise comparisons, yet the probability of an erroneous result is significant.
Bartlett’s Test and Levene’s Test are non-parametric checks for homogeneity of variances. Bartlett’s Test pretty much expects the underlying data to be normally distributed.
Levene’s Test is a better choice when you’re not sure the data is normal. Both are conservative and time-consuming to calculate.
We need another way to check for equal variances. [Read more…]
The data analysis course professor tended to focus on the practical application of statistics.
Avoiding statistical theory was fine with me. Learning statistics for me was on how to solve problems, optimize designs, and understanding data.
Then one lecture started with the question, “Why do we sum squares regression analysis, ANOVA calculations, and with other statistical methods?” He paused waiting for one us to answer.
We didn’t. I feared the upcoming lecture would include arcane derivations and burdensome theoretical annotations. It didn’t. [Read more…]
A common assumption when comparing three or more normal population means is they have similar (the same) population variances.
ANOVA and some DOE analysis results rely on the underlying data having similar variances. If this assumption is not true, the conclusions suggested by the ANOVA or DOE may be misleading.
It doesn’t take long to check. [Read more…]
Here the 2016 survey results as reported by the TypeForm survey tool. [Read more…]
Confidence intervals (CIs)provide a means to judge point estimates based on a sample from the population.
If that statement excites you, you may well have the makings of a fine statistician.
CIs are a form of internal estimate and specify a range within which a parameter may reside. CIs helps us evaluate the trustworthiness of point estimates. [Read more…]
