Your Reliability Engineering Professional Development Site
When looking at a pile of data, sometimes there is a data point that is not like the others. It attracts attention as it is different than the rest of the data.
When I spot something odd in a dataset, I wonder if there is something to learn here. Is this an opportunity to make a discovery or improve a process?
All too often it is tempting to remove the outlier as a mistake. Or to drop the outlier as it doesn’t make any sense and ‘messes up’ the analysis. [Read more…]
A risk management plan has to meet your organizations needs as the organization identifies, manages, and mitigates potential and actual risks. The ISO 31000 framework does not detail how an organization should plan or what elements are required for an effective plan.
This short article outlines a few details that may comprise a starting point as you build a risk management plan within your organization. Or, the suggestions here may help you review and improve your existing plan. [Read more…]
The Weibull distribution is a versatile tool to analyze time-to-failure data. Like any tool, it could be wielded well or not so well. In this short webinar, let’s discuss when and why you would use Weibull analysis, the basic process, and its limitations.
[Read more…]
The McNemar test is a nonparametric statistical test to compare dichotomous (unique) results of paired data.
If you are comparing survey results (favorable/unfavorable) for a group of potential customers given two ad campaigns, or evaluating the performance of two vendors in a set of prototype units, or determining if a maintenance procedure is effective for a set of equipment, this test permits the detection of changes.
The McNemar test is similar to the χ2 test. The McNemar only works with a two by two table, where the χ2 test works with larger tables. The χ2 test is checking for independence, while the McNemar test is looking for consistency in results.
Let’s examine an example where a group of people are surveyed about a prototype design, before and after a presentation. [Read more…]
Tim interviews Steven Wachs, a consultant, about his background and ongoing work as a statistician and consultant.
Kishor forward the link to the recording to me and suggest it may of interest to the Accendo Reliability community. I agree. He does a great job discussing the topic and a clear and practical manner.
The video abstract is:
IEEE Life Fellow and Hudson Chair in the Department of Electrical and Computer Engineering at Duke University Prof. Kishor S. Trivedi visited Alibaba’s Hangzhou campus on Dec. 8th and talked about technical systems’ reliability and availability assurance methods based on probabilistic models. [Read more…]
This is part of a short series on the common distributions.
The Triangle distribution is univariate continuous distribution. This short article focuses on 4 formulas of the triangle distribution.
The distribution becomes a standard triangle distribution when a = 0, b = 1, thus it has a mean at the $- \sqrt{{c}/{2}\;} -$ and the median is at $- 1-\sqrt{{\left( 1-c \right)}/{2}\;}-$. The distribution becomes a symmetrical triangle distribution when $- c={\left( b-a \right)}/{2}\;-$.
The triangle distribution is used to approximate distributions when the actual distribution is unknown and bounded, often useful for Monte Carlo simulations. Other applications include subjective representation when there is evidence of bounds and a mode, or as a substitution to the beta distribution since it is bounded. [Read more…]
Fred discussing the Speaking of Reliability podcast journey.
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This is part of a short series on the common distributions.
The Uniform distribution is a univariate continuous distribution. This short article focuses on 7 formulas of the Uniform Distribution. A common application is as a non-informative prior. Another application is to model a bounded parameter. The uniform distribution also finds application in random number generation. [Read more…]
This is part of a short series on the common life data distributions.
The Poisson distribution is a discrete distribution. This short article focuses on 4 formulas of the Poisson Distribution. It is also known as the rare event distribution. It has application in a homogeneous Poisson princess and with renewal theory. [Read more…]
In most organizations being a reliability engineer is a lonely position. I like to think we’re so effective that one or just a small team is all any an organization needs.
As with any engineering position, we have specialized training and skills. We view the world and problems just a little differently than others. Then we use statistics, which tends to future isolate us from our peers.
For over 50 years there have been professional societies focused on supporting the professional education of reliability engineers. For nearly as long there have been trade journals and newsletters. Longer for technical journals and other scientific and engineering organizations and journals. [Read more…]
This is part of a short series on the common life data distributions.
The Pareto distribution is a univariate continuous distribution useful when modeling rare events as the survival function slowly decreases as compared to other life distributions. This short article focuses on 7 formulas of the Pareto Continuous Distribution also known as the Pareto distribution of the first kind (there are three kinds, apparently). [Read more…]
The quality triangle is an admission that every project has design constraints. Note that reliability is rarely listed directly within the triangle, yet reliability does impact each element of the triad.
In a world where the design team is beset with numerous design for X priorities, understanding how reliability related to the top three and most common set of constraints is imperative.
For any set of priorities, you should be able to convey how addressing reliability performance in the design and assembly process impacts those priorities. Addressing reliability can improve quality, reduce the risk of launch delays, and reduce the cost of a product. All this and improve customer satisfaction as well.
Let’s explore a few ways you can connect reliability priorities to the common quality triangle priorities. [Read more…]
We make decisions every day. Our project teams and organizations have many individuals making decisions every day. Most of these decisions have little to do with product reliability, yet a surprising number of design, marketing, production, and customer care decisions that have a direct impact on product reliability performance.
As a reliability professional, do you work to make better decisions? Do you work to enable the individuals designing, producing, marketing, etc your organization’s products to make better decisions concerning reliability?
If not, why?
Let’s outline a few ways to estimate the value to you and your organization to improve decision making concerning reliability. [Read more…]
This is part of a short series on the common life data distributions.
The Binomial distribution is discrete. This short article focuses on 4 formulas of the Binomial Distribution.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]