Reliability is Green
Abstract
Kirk and Fred discussing a recent opinion article in the New York Times on technology waste.
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SOR 656 Reliability is Green
Speaking of Reliability
Let us address your reliability engineering questions. Gain the experience of your peers and accelerate the improvement of your program and career.
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SOR 655 Hints to Have a Proactive Reliability Program
Hints to Have a Proactive Reliability Program
Abstract
Chris and Fred discuss ‘proactive’ reliability engineering … and trying to get out of being ‘reactive.’ Some entire organizations are structure around waiting for the catastrophe to happen before we fix it. So changing perspectives can be challenging.
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SOR 654 Reliability Starts at the Top
Reliability Starts at the Top
Abstract
Chris and Fred discuss how reliability starts at the top of every organization. And by that, we mean … leadership. What does that mean? How can the CEO be more important for reliability performance than the reliability engineer with decades of experience and post-graduate qualifications?
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SOR 653 More Than One Root Cause
More Than One Root Cause
Abstract
Carl and Fred discussing the subject of root cause. How many “root causes” can there be? Is there only one “root cause” for a problem? What about events or conditions that happen in tandem?
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SOR 652 Reliability and Risk Management
Reliability and Risk Management
Abstract
Carl and Fred discussing the relationship of reliability and risk management, including areas of overlap and linkages.
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SOR 651 Suspected or Known Problem
Suspected or Known Problem
Abstract
Kirk and Fred discussing the challenge of finding a latent problem in a product after your company has been producing and selling for some time.
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SOR 650 Reliability Expectations
Reliability Expectations
Abstract
Kirk and Fred discussing how we continue to evolve our reliability expectations as technology becomes more robust and reliable.
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SOR 649 Extrapolation and Sample Sizes
Extrapolation and Sample Sizes
Abstract
Chris and Fred discuss (essentially) how many ‘things’ you need to see to know enough about those ‘things.’ We see this conundrum across all sorts of fields of study. How many kangaroos do I need to capture and weigh to get a good understanding of the entire population’s typical weight? Delete kangaroo and insert whatever thing matters to you.
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SOR 648 Accelerated Testing Assumptions
Accelerated Testing Assumptions
Abstract
Chris and Fred discuss accelerated testing. Accelerated testing is great for getting lots of information in a short period of time. You can compress a lot of ‘real-time’ life into a really small amount of testing. But how do you do it right? You need to understand the underlying physics of failure to work out how the compression of time works.
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SOR 647 Deterministic versus Probabilistic
Deterministic versus Probabilistic
Abstract
Chris and Fred discuss what it means to be ‘deterministic’ versus ‘probabilistic’ … and what that means for reliability engineering. Know what these words mean and want to learn more? Don’t know what these words mean and want to understand how they could help reliability engineering? Listen to this podcast.
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SOR 646 The Hunt for Reliability Training
The Hunt for Reliability Training
Abstract
Chris and Fred discuss how you go about ‘hunting’ reliability training. And we do mean ‘hunt.’
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SOR 645 Standards and Regulations
Standards and Regulations
Abstract
Kirk and Fred discussing the role and relationship of meeting standards and making a reliable product.
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SOR 644 Dealing with Failures that Destroy
Dealing with Failures that Destroy
Abstract
Kirk and Fred discussing the challenge of understanding the root cause of failures for products that are hard to retrieve from the field or have other reasons that make failure analysis almost impossible.
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SOR 643 Maintenance FMEAs
Maintenance FMEAs
Abstract
Carl and Fred discussing the application of FMEAs in support of asset maintenance.
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SOR 642 What Price Reliability?
What Price Reliability?
Abstract
Carl and Fred discussing the reliability of the Texas electric grid debacle, and what can be learned from a reliability point of view. Lessons learned from this problem can be applied to other industries.
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