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Speaking Of Reliability: Friends Discussing Reliability Engineering Topics | Warranty | Plant Maintenance
Speaking Of Reliability: Friends Discussing Reliability Engineering Topics | Warranty | Plant Maintenance episodes
- SOR 628 HALT & Activation EnergyHALT & Activation Energy Abstract Carl and Fred discussing a listener-submitted topic about highly accelerated life testing. Key Points Join Carl and Fred as they discuss the difference between ALT and HALT, what models can be used, and the limitations. Topics include: HALT is intended to find product weaknesses, not a life test HALT will […]0 min
- SOR 627 Selecting Tools for a PlanSelecting Tools for a Plan Abstract Carl and Fred discussing how to select the tools that will be used in reliability plans. Key Points Join Carl and Fred as they discuss what makes for good reliability plans. Topics include: Wrong way: selecting the tools that you have always used, merely because they have been used […]0 min
- SOR 626 The Dangers of Group ThinkThe Dangers of Group Think Abstract Carl and Fred discussing the dynamics of teams, how they work, and what can go wrong. Key Points Join Carl and Fred as they discuss the dangers of “group think” when working with teams. Topics include: What is “group think”? If everyone agrees with an idea, maybe somebody is […]0 min
- SOR 625 Common SenseCommon Sense Abstract Carl and Fred discussing the role of common sense in reliability engineering and management. Key Points Join Carl and Fred as they discuss how faulty thinking that defies common sense can lead us in the wrong direction. Topics include: What is “common sense”? Why is it important? Every failure matters, and should […]0 min
- SOR 624 Warranty and ReliabilityWarranty and Reliability Abstract Chris and Fred discuss what warranty and reliability mean for each other. They sounds like very related topics or ideas. But are they? Listen to this podcast to learn more. Key Points Join Chris and Fred as they discuss warranty and warranty management. Is this different to reliability? What do you […]0 min
- 6 Sigma and Reliability6 Sigma and ReliabilityAbstract
Chris and Fred discuss what the idea of ‘6 sigma’ means for reliability. What is ‘6 sigma?’ Is it a buzzword? There are lots of different ideas on what ‘6 sigma’ is. We are always on the look out for amazing new things that are going to help us do reliability stuff. Want to learn more? Listen to this podcast.
Key PointsJoin Chris and Fred as they discuss ‘6 sigma.’ What is ‘6 sigma?’ Well … it’s complicated. Because many different people have different ideas on what it means.
Topics include:
- What is ‘6 sigma?’ It is an idea that is based on manufacturing components that are so far inside design tolerances that they won’t ever approach ‘unsatisfactory’ dimensions that the ultimate product will be very reliable. And it can also be looked at as designing something that is so robust that manufacturing inaccuracies will have a limited effect on overall reliability.
- But what about other ideas? Like ‘6 sigma’ being a methodology where you create a team to solve a problem. How is this a ‘6 sigma’ thing only? It’s not!
- So where does the title of ‘6 sigma’ come from? The idea is that the normal distribution (bell curve) can be described by its standard deviation – something we often represent with the Greek letter ‘sigma.’ So the idea is that the variation in the dimensions of something you are manufacturing IS modeled by a bell curve, and that we want tolerances (or ‘bad’ dimensions) to be ‘6 sigmas’ away from the mean value. BUT … not everything is modeled by a bell curve – especially at the extreme values that matter when it comes to things being out of specification. And because of these assumptions and underlying statistics, people who can’t ‘handle’ the statistics simply assume statistical principles … and go back to building teams to solve problems.
- So how does ‘6 sigma’ relate to reliability? Well, it is great if ‘6 sigma’ means you work out how your thing can fail (like a FMEA), identify where manufacturing will contribute to failure, and then set tolerances to prevent this, then ‘6 sigma’ or any other school of thought that focuses on high-quality manufacturing will contribute to reliability. But if ‘6 sigma’ is reduced to a philosophy for creating teams that solve problems … then what is so special about it?
Enjoy an episode of Speaking of Reliability. Where you can join friends as they discuss reliability topics. Join us as we discuss topics ranging from design for reliability techniques to field data analysis approaches.
Download Audio RSSShow NotesThe post SOR 623 6 Sigma and Reliability appeared first on Accendo Reliability.
0 min - Does a CMMS Lead to a Process?Does a CMMS Lead to a Process?Abstract
James and Fred discussing adopting and using a CMMS system.
Key PointsJoin James and Fred as they discuss a few key points to consider when installing or changing a CMMS.
Topics include:- Avoid trying to alter your process to fit a CMMS – that will lead to workaround and problems
- Get a system that actually has the features you need, and not the fancy stuff you don’t
- Remember that bad data into the system is not the fault of the software
Enjoy an episode of Speaking of Reliability. Where you can join friends as they discuss reliability topics. Join us as we discuss topics ranging from design for reliability techniques to field data analysis approaches.
Download Audio RSSShow NotesThe post SOR 622 Does a CMMS Lead to a Process? appeared first on Accendo Reliability.
0 min - Considering Vibration SensorsConsidering Vibration SensorsAbstract
James and Fred discussing a few best practices when considering installing vibration sensors.
Key PointsJoin James and Fred as they discuss sensors and when and why to use them, prompted by a call from a vibration sensor salesman.
Topics include:- For your equipment does monitoring vibration help?
- Gathering data is great, if and only if it is actually used.
- A sensor signally an item needs maintenance attention without a solid planning/scheduling program leads to a larger, and frustrating, backlog
Enjoy an episode of Speaking of Reliability. Where you can join friends as they discuss reliability topics. Join us as we discuss topics ranging from design for reliability techniques to field data analysis approaches.
Download Audio RSSShow NotesThe post SOR 621 Considering Vibration Sensors appeared first on Accendo Reliability.
0 min - Rational AssumptionsRational AssumptionsAbstract
Chris and Fred discuss the roles assumption play in reliability. Assumptions can be very useful … or very dangerous. Why? Because assumptions are information. If they are based on nothing, then assumptions become misinformation. Which means you make the wrong decision. Listen to this podcast to learn more!
Key PointsJoin Chris and Fred as they discuss the role assumptions play in (reliability) engineering. Why are assumptions so prevalent? Because assumptions are information. So if you have no idea about something, making an assumption essentially throws information at your problem. Which is great! If the assumption is valid. And if it is not? Then you have misinformation.
Topics include:
- Assumptions can be great! In fact, most statistical analysis requires you to assume a model that describes the data you are seeing. If you are really making a ‘guess’ … then always go back and validate your assumption. This is really easy …
- What happens if you get it wrong? You are essentially making decisions for ‘Product X’ based on characteristics of some – potentially fictitious – ‘Product Y.’ Which is dangerous.
- WeiBayes … what is this? The way it is used today … really bad. A Weibull distribution has two parameters and is often assumed to model failure data. Bayesian analysis involves bringing prior, ‘non-test’ information to your analysis to improve the outputs. And what is WeiBayes? Simply assuming one of the parameters for your Weibull distribution is a fixed value. Again … this is an assumption. And those of you who know what Bayesian analysis is truly about, know that this is a dangerous oversimplification of that approach.
- Assumptions don’t change the underlying physics and chemistry of failure. They can make our life simpler. There was once a (so-called) reliability engineer who tried 15 different models to see which one meant they needed the fewest samples for testing. And then they assumed that model described the time to failure of their product. Flat out ridiculous.
- But don’t be afraid to make assumptions if you have the information. Let’s say that you are looking at a new bearing. Well … not a new bearing. The same bearing with a different lubricant. You don’t have a lot of bearings to test. So perhaps you are going to assume the shape parameter that describes the nature of the time to failure of the ‘old’ bearings won’t change. And this is often a valid assumption. ‘Shape’ parameters tend to describe the nature of failure, while ‘scale’ parameters describe the typical or characteristic time it takes for your bearing to fail. Making this assumption can reduce the number of bearings you need to test. And if you really want to do Bayesian analysis properly, you can put what we call a prior distribution into your model which describes the likely values of the shape parameter. This is smart.
- Do you assume models and distributions to make your life easier – or because they are based on evidence?
Enjoy an episode of Speaking of Reliability. Where you can join friends as they discuss reliability topics. Join us as we discuss topics ranging from design for reliability techniques to field data analysis approaches.
Download Audio RSSShow NotesThe post SOR 620 Rational Assumptions appeared first on Accendo Reliability.
0 min - Is Testing The Way to Confirm ReliabilityIs Testing The Only Way to Confirm ReliabilityAbstract
Chris and Fred discuss how we confirm the reliability of something we are making. Or maintaining. Or managing. This is in response to someone raising a question regarding reliability allocation – based on an Accendo webinar. And the question was all about working out how to test that we are on track to meet goals allocated to subsystems and components. So what do we do? Well listen to this podcast!
Key PointsJoin Chris and Fred as they discuss a question that one of our listeners asked us about testing the bits of a system that have had reliability goals allocated to them. To be specific, our listener was talking about MTBF – which is problematic. But there are some key problems that all of us face when trying to work out if we are on track!
Topics include:
- It is exceedingly rare for there to be NO data or information about a component. Which means that testing should be our last resort. So why pretend no information exists? If one of your components or subsystems has been used in lots of previous models or versions … then you have run the best test possible! … by your customers! … use that data!
- … and it is exceedingly difficult to demonstrate really high reliability. So if you have a reliability target of 99.9998 % … do you realize how many components you need to test to demonstrate that you have met this target with a 90% confidence level? A lot.
- It is actually about reliability analysis. Not reliability testing. This is not semantics. Reliability analysis takes all forms of evidence, information, data and expert judgment you have at hand. And if you have none of this … then you test. We know how metal fails. The two Space Shuttle disasters were caused by very well known failure mechanisms – not some weird ‘space ray’ or anything else that is not of this world. So let’s use the knowledge we have already gathered.
- But if you are forced to test for MTBF, then there is an ‘industry’ standard approach. Which is very, problematic. But let’s go through the steps for creating the test plan.
1. The first thing you do is assume a constant hazard rate. Which is terrible.
2. Then you work out your MTBF goal.
3. You then need to assume that the product under test is more reliable than your goal. That’s right. And we call this the discrimination ratio (DR). So if you assume a DR of 2, then you will be creating a test plan assuming that your product has an MTBF twice that of your goal (if you don’t do this, then there is very little chance for your thing passing the test.’)
4. You then need to quantify the risk that an unreliable product will pass your test. This is always statistically possible. So you have to limit it. Let’s say we are happy to accept a 5 % risk. Lets call this risk ‘β‘.
5. Then (start) by assuming a certain number of allowable failures. Lets start with 0 … which gives us the shortest test duration! Let’s call the acceptable number of failures ‘r.‘
6. Then calculate the test duration using the following equation
$$T=\frac{MTBF_{goal} \times {\chi}^{2}_{(1 – \beta ; 2r+2)}}{2}$$
… where Χ2 is the ‘chi-squared’ random variable which has a CDF value of β and 2r + 2 ‘degrees of freedom.’ Explaining this one is the subject of a whole other podcast, but Excel can help you out!
7. But … now you need to work out the probability of actually passing the test if you have a product which exceeds your requirement. And you use this equation:
$$\alpha = 1 – F_{Poisson}(r ; \mu = \frac{T}{DR \times MTBF_{goal}})$$
where α is the risk of your really reliable thing not passing the test and FPoisson is the CDF of the Poisson distribution … again Excel can help you out!
8. Realize that the risk of not passing the test is way to high … and then go back to step 5. And increase the allowable number of failures. This will lengthen the test, but also reduce the risk of your really reliable thing not passing the test. And you keep doing this until you have enough allowable failures to reduce the risk to something you are happy with. And this is the number of samples you need!
- … but you can test smarter! Find the vital few ways your thing can fail. And then focus (perhaps accelerated) testing on the dominant failure mechanism only. This will save lots of time and money. And of course, assuming a constant hazard rate is just dumb.
Enjoy an episode of Speaking of Reliability. Where you can join friends as they discuss reliability topics. Join us as we discuss topics ranging from design for reliability techniques to field data analysis approaches.
Download Audio RSSShow NotesThe post SOR 619 Is Testing The Only Way to Confirm Reliability appeared first on Accendo Reliability.
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