Your Reliability Engineering Professional Development Site
There are two basic approaches when addressing product reliability: Reactive or Proactive. Neither is right or wrong.
The reactive approach has the limitation of responding to failures that occur during prototyping, production and customer use.
The proactive approach focuses on preventing product design, assembly, and use issues before they have the opportunity to create a product failure.
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.
A common expectation consumers have concerning products is that the product works. The product provides value by performing one or more functions. Also, implicit with this expectation is the product will function over some duration. A reliable product meets or exceeds this common expectation.
Every product has a finite duration of successful operation before failure occurs.
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…]
In Katmandu, I visited a small pottery factory. There was a young man sitting at a potting wheel making candle stands. He didn’t measure anything and I doubt anyone did.
Based on experience and just looking at a finished item, he could tell if it was acceptable or not. That was good enough.
Reliability coaching is one-to-one or small team discussions lead by an experienced reliability engineer (me) focused on achieving an agreed upon goal for the coaching program.
When I first started working in a factory as a shift supervisor, it seemed there was some type of formal training nearly every month. And we were expected to attend a conference once a year. Little did I know that was a great time to start working in industry. We enjoyed a lot of great training.
Later in my career and as the economy changed budgets for training slowly declined. Travel budgets also slipped away. I don’t have hard numbers, yet I suspect there is less company-sponsored professional development than 20 years ago.
As an engineering working in the reliability field, how do you keep up and learn what you need to know to accomplish your work? [Read more…]
It always seems there are more red lights when I’m running late.
This comment and others similar to it caught my attention lately. Is it just Murphy’s Law or something else?
There is an element of luck or chance in many events during our daily lives. The same is true when producing products and the resulting reliability. A lot has to go right for the product to work for the customer.
When something isn’t going as expected, we have a comparison that gains attention. If the lights are red more often, in theory, when I’m running late, maybe the lights are not turning red more often, it’s just that we notice.
For product development, it’s noticing the items that are going well and failing that matter. As reliability professionals, we need to continue to practice being aware of what is expected and what actually happens. [Read more…]
This non-parametric analysis tool provides a way to compare two sets of ordinal data (data that can be rank ordered in a meaningful manner). The result, rs, is a measure of the association between two datasets.
You may want to know if two reviewers have similar ratings for movies, or if two assessment techniques provide similar results. [Read more…]
Let’s say we have data that is only rank order from two or more evaluators (people, algorithms, etc.) and we want to determine if the evaluators agree or not.
The agreement here meaning the results from one person or another are in agreement, or they are concordant. This is typically done with this non-parametric method for 3 or more evaluators. For a comparison of two evaluators consider using Cohen’s Kappa or Spearman’s correlation coefficient as they are more appropriate. [Read more…]
Reliability engineers learn basic tools and techniques most often from others with that knowledge. During our careers, we also continue to learn and often find ourselves teaching. Even when mentoring we find ourselves learning. Being a deliberate and active student and teacher is a great way to remain inquisitive and helpful.
Formal education will make you a living; self-education will make you a fortune. [1]
Learning never stops. [Read more…]
Let’s explore finding the value of reliability tasks and how to articulate the importance of those activities in business terms.
Estimating the return on investment for risk assessment or accelerated testing can help you justify the investment in these and similar tools.
This webinar discusses the importance of estimated value and helps you discover and state the value of common activities.
Put a number on the value of tasks that you bring to the organization with your work.
Detecting a change or difference is often the aim of an experiment or set of measurements. We want to learn which vendor, process, or design provides a better result.
When we use a sample to estimate a statistic for a population we take the risk that the sample provides values that are not representative of the population. For example, if we use a professional basketball team to sample men’s height. We may conclude that the height of men in the general population is taller than the true population value. [Read more…]