Failure Rate to a Distribution
Abstract
Chris and Fred discussing a listener’s questions on how to convert a failure rate to a distribution.
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SOR 863 Failure Rate to a Distribution
SOR 862 Dealing with Reliability Risk
Dealing with Reliability Risk
Abstract
Greg and Fred discussing how reliability inherently involves variability and risk.
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MM S5:E09 John Oskin
John Oskin
In this week’s episode, John Oskin, CEO of Sage Clarity Solutions, emphasizes the need to use OEE to identify losses and stratify them to determine their causes, recommends excluding planned maintenance from OEE calculations, and stresses the importance of real-time feedback and intelligent root cause analysis using AI and digital twin concepts. Oskin also notes that while AI is changing every industry quickly, there is still a human element required to understand and use the data. The biggest takeaway that John hopes listeners will take from the discussion is to start taking action today to advance their asset management digitization strategy.
QDD 109 Before You Start Engineering Solutions, Do This
Before You Start Engineering Solutions, Do This
We’re given information about an opportunity for a new product.
We talk about what can happen when we start solution-building just with what we’re given. And we talk about an alternative start to a new engineering project.
SOR 861 Reshoring and Talent
Reshoring and Talent
Abstract
Greg and Fred discussing bringing suppliers back to the location of OEM headquarters and factories. A common risk is finding the talent to design and build these factories.
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SOR 860 Software and Thinking
Software and Thinking
Abstract
Chris and Fred discuss the many and varied different software package that can help you do ‘reliability stuff’ … and how we usually assume everything they do is ‘OK.’ But how do we know the software is giving us the numbers we need?
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MC 011 Tribute to Steel Metallurgy Knowledge
Tribute to Steel Metallurgy Knowledge
In this episode, I discuss the state of steel metallurgy knowledge and the things this knowledge has enabled, including ultra-high volume steel production and the ability to engineer components with a wide range of properties for a wide range of applications.
To learn more about this subject, check out the article A Tribute to Steel Metallurgy Knowledge.
RiM 21: Simplifying Hypotheses Testing with Sandeep
Simplifying Hypotheses Testing with Sandeep
In this episode, I speak with Sandeep Jadhav about Hypothesis Testing. We talk about what Hypothesis Testing is and why we need it.
If you want to understand what the “p” value is and interpret that, you’ve come to the right place. Sandeep explains the concept of the Null alternate hypothesis as well as the producer’s risk or as he likes to call it an overreaction in the simplest way possible. If you were ever apprehensive about learning about Hypothesis Testing then this is the podcast for you.
If you want to learn more about Six Sigma and Hypothesis Testing, feel free to reach out to Sandeep through LinkedIn.
QDD 108 QDD Redux Ep. 4: Statistical vs. Practical Significance
QDD Redux Ep. 4: Statistical vs. Practical Significance
In this special episode of Quality during Design Redux, we’re pulling episodes from our archive about test results analysis.
In our Season 1 – Episode 93 titled “The Fundamental Thing to Know from Statistics for Design Engineering”, we talked about hypothesis testing: how it is used for lots of data analysis techniques.
When we’re looking at results (like measures of a characteristic), we need to take care not to get too hung-up on what the statistics is trying to tell us. Yes, statistical tools are a good way for us to make decisions and the results can act as proof for us. But, there’s a practical, engineering side to results, too. We need to evaluate the statistical significance along with the practical significance.
We review an example and how to document it.
Communicating as a Reliability Engineer
Communicating as a Reliability Engineer
podcast episode with speaker Fred Schenkelberg
Creating a plan and generating information is part of reliability engineering, yet it’s not enough. To be a successful engineer, one must communicate well. This means we need to write, discuss, and present well. We are often called upon to examine failures and recommend solutions, examine a dataset and explain the finding, or conduct an experiment and detail the results. [Read more…]
RM 119: Quantum Mechanics Explained
Quantum Mechanics Explained
I first met my guest at the SMTA Pan Pacific Symposium in Hawaii this past January. He was presenting a paper entitled Quantum Technology, A Theoretical Overview of the Possibilities. The more I listened to and watched his presentation, the more I wanted to learn about quantum physics and mechanics. So I selfishly invited him onto my show today so I could learn more, and perhaps you can too.
My guest today is Dr. James Whitfield. Dr. Whitfield is an associate professor of physics at Dartmouth. He earned his Bachelor’s of science and chemistry and mathematics from Morehouse University and his PhD in chemical physics from Harvard University. He was a postdoctoral fellow at Columbia University in New York, Vienna Center for Quantum Science and Technology in Vienna and Gant University and Belgium, and he is currently an Amazon visiting academic and even better than all that, he’s my guest today on the Reliability Matters Podcast.
SOR 859 An ALT Design Question
An ALT Design Question
Abstract
Chris and Fred discuss an ALT or Accelerate Life Test Design Question. We love these podcasts … as we are directly answering a question from one of our listeners. Interested in hearing a response to a real-world question from a listener?
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SOR 858 Reliability Predictions and FMEA Occurrence
Reliability Predictions and FMEA Occurrence
Abstract
Carl and Fred discussing a reader question about FMEAs. Specifically, whether reliability predictions (for similar systems) are valid input to the Occurrence rating in an FMEA.
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QDD 107 QDD Redux Ep. 3: When it’s Not Normal: How to Choose from a Library of Distributions
QDD Redux Ep. 3: When it’s Not Normal: How to Choose from a Library of Distributions
When trying to fit a probability distribution to quantitative results, sometimes the normal probability doesn’t fit. Minitab has a wealth of distributions to pick from. Do you just pick whichever one Minitab tells you fits the best? Maybe not. Just because the distribution fits your data doesn’t mean it’s a good one to use. We review my top 3 distributions for product testing and some other ones that come up but may not be appropriate to use.
We’ll also share what you need to think about when picking a distribution:
- think about your purpose of test
- consider your failure mode and how you’re expecting your product to perform – does the typical use case of a distribution fit?
- when you have options, the simpler the distribution the better (i.e. choose a 2-parameter over a 3-parameter)
SOR 857 Selecting the Right Method
Selecting the Right Method
Abstract
Greg and Fred discussing the right method (s) to solve quality and reliability problems specifically answering the question ‘is the approach good enough?’
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