Speaking Of Reliability: Friends Discussing Reliability Engineering Topics | Warranty | Plant Maintenance

FMEA and Hazard Analysis Abstract Carl and Fred discuss a reader question about FMEA and Hazard Analysis and whether or not they can be combined into a single analysis. Key Points Join Carl and Fred as they discuss the difference between FMEA and Hazard Analysis. Topics include: FMEA and Hazard Analysis have some similarity of teams; difference is focus on safety Hazard Analysis focuses on safety risk, whereas FMEA focuses on all both performance and safety risk Design FMEA has important input to HA Risks assessment is different between FMEA and HA HA uses different risk assessment FMEA and HA: separate meetings Analysis must pass regulatory needs, but that is not primary function Too much emphasis on HA without FMEA can miss important reliability issues There is a role for both techniques Baked-in failure modes are very expensive to fix Reliability planning sorts out which methods are needed Results of HA can go back into FMEA 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 RSS Show Notes   The post SOR 996 FMEA and HAZOP appeared first on Accendo Reliability.

Hidden Reliability Abstract Carl and Fred discuss the challenges of hidden reliability problems, especially issues that are certain to occur, but not easily observed. When failures are invisible or hidden, they can be missed or ignored. Key Points Join Carl and Fred as they discuss the importance of making reliability issues visible. Topics include: “If it isn’t broke, don’t fix it” Part of reliability’s job is to make potential failures visible to users and management before they become catastrophic This is why preventive maintenance is important How to make accumulated damage or hidden failures more visible It is very expensive to wait until a failure to fix the problem Implemeting Prevention takes a company culture Deferred maintenance Deming quote (see Show Notes) 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 RSS Show Notes Deming quote from “Out of the Crisis” “One gets a good rating for fighting a fire. The result is visible, can be quantified. If you do it right the first time, you are invisible. Mess it up and correct it later, you become a hero.” Factor of Ten Rule   The post SOR 995 Hidden Reliability appeared first on Accendo Reliability.

Introducing Risk Management Plan Abstract Greg and Fred discuss why risk is becoming a personal issue to home owners and to all of us.  They discuss aging infrastructure risk, who pays, and how to mitigate these risks. Key Points Join Greg and Fred as they discuss infrastructure risks of aging water, sewage, and power systems.  Topics include: What are types of infrastructure risk. How do you get folks’ attention about risks. How to do ‘what if’ analysis. 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 RSS Show Notes   The post SOR 994 Introducing Risk Management Plan appeared first on Accendo Reliability.

What Happened to Quality? Abstract Greg and Fred discuss quality from engineering and quality points of view.  Greg is developing AI engineering applications.  Greg wants to build, ship, and monetize.  Fred wants to build quality in.  What do you think is the right way? Key Points Join Greg and Fred as they discuss what’s happened to quality from critical perspectives.  Topics include: State of today’s quality profession ‘Show me the money’ approaches ‘Build quality and reliability from the get go’ approach Enjoy an episode of Speaking of Reliability as sparks fly. 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 RSS Show Notes   The post SOR 993 What Happened to Quality? appeared first on Accendo Reliability.

Past Good and Bad Knowledge Abstract Chris and Fred discuss the so-called ‘bedrock documents and statistics’ that are used over and over again as if they are universally correct – even though they might have nothing to do with ‘your’ machines or systems. WHY? Key Points Join Chris and Fred as they discuss the role that documents that are quoted as if their figures and conclusions they contain are unambiguously correct, for all machines, for all time. Why? Topics include: ‘I want a quick answer and don’t want to think too much about it.’ If this sounds like you … we can’t help. You are the type of person who likes the documents we are talking about. Please seek therapy. If you want to be a designer or an engineer … be a designer or an engineer. Oh … Artificial Intelligence (AI) is a tool. Not a god. Give me an example. There is a report authored by Nowlan and Heap that multiple industries use to say things like ’89 % of components never wear out.’ The trouble is that Nowlan and Heap’s report contains reliability curves that don’t make sense (like a straight line going from 100 to 0 %, which can’t happen), based on data that is incomplete as components were being replaced early on in their lives (never allowing them to wear out and produce wear out data), and focused on United Airline’s fleet of aircraft circa 1978 (which isn’t shared, so we can’t interrogate it). Aircraft are around 10 times more reliable today as they were then, so even for today’s airlines these figures would be meaningless. But Nowlan and Heap’s report is frequently cited to this day as if it is just as authoritative on things like power-generating plants and manufacturing facilities. But we have been using these figures forever and everything is fine. Really? What does fine mean to you? Making the same mistakes over and over again and having this reality normalized? If you want to be competitive, take the time to understand your machines. This will help you make decisions that would almost certainly drastically improve reliability and availability through things like adjusting maintenance regimes and so on. 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 RSS Show Notes The post SOR 992 Past Good and Bad Knowledge appeared first on Accendo Reliability.

What is MLE? Abstract Chris and Fred discuss the three-letter acronym ‘MLE’ stands for? Well, it stands for ‘maximum likelihood estimate.’ Ever heard of it? Do you know what it means? Key Points Join Chris and Fred as they discuss what the MLE or ‘maximum likelihood estimate’ means … usually when using software to conduct data analysis. Topics include: What does ‘likelihood’ mean? Most models (like the bell curve, lognormal distribution, and so on) are defined by parameters. For example, the two parameters that define the bell curve are (1) the mean and (2) standard deviation. So these two parameters entirely describe the shape of the bell curve. Now let’s say you have 20 data points from some random process. What is the ‘likelihood’ that a bell curve with a mean of 10 and a standard deviation of 2 ‘fits’ your data? It has been found that if we plot out all 20 data points under this bell curve we think might fit the data, and then draw lines up from each data point to the bell curve shape … and then multiply those heights – we get the ‘likelihood’ that the bell curve fits your data. Or perhaps more correctly, we get a number that represents the ‘likelihood’ that the bell curve models the random process that gave us those 2o data points. What does ‘maximum likelihood estimate’ mean? Let’s say we are not sure if the bell curve above, or another bell curve with a mean of 9.5 and standard deviation of 1.5 is a good fit. Well … we can go through the same process for the other bell cruve, and come up with another number that represents its ‘likelihood.’ Now we can try all possible combinations of means and standard deviations and come up with likelihoods for each. Computers are good at this, and they can (for example) work out that a bell curve with a mean of 9.76234… and standard deviation of 1.8986… will have the highest likelihood of all possible bell curves. This is the ‘maximum likelihood estimate.’ It’s just another way of coming up with our ‘best fit’ or ‘best guess.’ Other approaches involve ‘regression analysis’ or ‘root mean square analysis’ where we find a line that minimizes the squares of the ‘residual’ distances between candidate lines of best fit and the data points. So my software package allows me to choose from lots of different options … including MLE … which should we use? WHAT DECISION ARE YOU TRYING TO MAKE? There are no absolute guidelines that work for every scenario. The first question you should ask is … do you need the ‘best guess’ of something or the region within which you are ‘confident’ that something lies? For example, the ‘best guess’ at your item’s time to failure might be 5.4 years. But you might only be 95 % confident that the time to failure will exceed 1.9 years. If you are trying to understand the likelihood that your item will fail within a 2-year warranty period, you might not be interested in your best guess, but instead be interested in a 95 % confidence region. 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 RSS Show Notes The post SOR 991 What is MLE? appeared first on Accendo Reliability.

Impact of IoT on Maintenance Abstract Kirk and Fred discuss the impact of the Internet of Things (IoT) on maintenance and the data feedback to the manufacturer on usage and failure data. Key Points Join Kirk and Fred as they discuss on the new capabilities of products to connect with the Internet and how they open up more detailed and widespread details on the actual field use, health prognostics, and failure modes. Topics include: Automobiles, and especially the new electric vehicles, now provide so much data on driving actions such as acceleration, braking, and other use conditions that they are much more useful for improving the design from the previous model year. Telemetry that is now built into capital manufacturing equipment and systems that can provide prognostics on system wear and degradation can prevent unscheduled and costly downtime. Many manufacturers are including IoT in their devices and are receiving massive amounts of data that must be parsed and analyzed to derive valuable prognostics for improving maintenance and design 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 RSS Show Notes Please click on this link to access a relatively new analysis of traditional reliability prediction methods article from the US ARMY and CALCE titled  “Reliability Prediction – Continued Reliance on a Misleading Approach”. It is in the public domain, so please distribute freely. Trying to predict reliability for development is a misleading a costly approach. You can now purchase the most recent recording of Kirk Gray’s Hobbs Engineering 8 (two 4 hour sessions) hour Webinar “Rapid and Robust Reliability Development 2022 HALT & HASS Methodologies Online Seminar” from this link. For more information on the newest discovery testing methodology here is a link to the book “Next Generation HALT and HASS: Robust design of Electronics and Systems” written by Kirk Gray and John Paschkewitz. The post SOR 990 Impact of IoT on Maintenance appeared first on Accendo Reliability.

No Trouble Found Issue Abstract Kirk and Fred delve into the crucial topic of managing returned parts and products, even those that are functioning perfectly. Key Points Join Kirk and Fred as they discuss why parts or products returned to the manufacturer seem to have no failure on their test bench. Topics include: The cost of production loss can be significant for capital production equipment failures. A field service agent may replace many circuit boards or components, known as “shotgunning,” the system when only one of the replaced parts has caused the failure. Undoubtedly, there is a lot of noise when evaluating returned systems or parts identified as failed. Some failure mechanisms, such as fretting corrosion, are from poor contact plating or contamination in connectors, and re-seating or replacing the connector removes the cause of failure. Only through further analysis of the connector’s contact surfaces can it be diagnosed Companies that are dismissive and blame the customer for misuse, dismiss failures as expected “infant mortality,” and do not perform detailed failure analysis may perpetuate a costly hidden design or manufacturing defect. The value and concern companies have for the reliability of their products depend heavily on the industry’s expectations. Everyone expects high reliability in safety-related industries such as aircraft, where pinball machines’ unreliability has been historically accepted. 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 RSS Show Notes Please click on this link to access a relatively new analysis of traditional reliability prediction methods article from the US ARMY and CALCE titled  “Reliability Prediction – Continued Reliance on a Misleading Approach”. It is in the public domain, so please distribute freely. Trying to predict reliability for development is a misleading a costly approach. You can now purchase the most recent recording of Kirk Gray’s Hobbs Engineering 8 (two 4 hour sessions) hour Webinar “Rapid and Robust Reliability Development 2022 HALT & HASS Methodologies Online Seminar” from this link. For more information on the newest discovery testing methodology here is a link to the book “Next Generation HALT and HASS: Robust design of Electronics and Systems” written by Kirk Gray and John Paschkewitz. The post SOR 989 No Trouble Found Issue appeared first on Accendo Reliability.

Difference Between Quality and Reliability Statistics Abstract Philip and Fred discuss some of the basic differences and similarities between these two types of statistical toolsets. Key Points Join Philip and Fred as they discuss why it’s best to fully understand and use all the available statistial tools when faced with either quality or reliability related issues. Topics include: The simple differences due to the nature of the data. The common elements underlying both toolsets. A few examples how quality tools are good for reliability outcomes, and vice versa. 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 RSS Show Notes   The post SOR 988 Difference Between Quality and Reliability Statistics appeared first on Accendo Reliability.

Dealing with Missing Data Abstract Philip and Fred discuss a few challenges and approaches to deal with missing data. Key Points Join Philip and Fred as they discuss what can be done when a dataset has missing data. Topics include: Data collection and storage issues Dealing with data points within the overall dataset Dealing with missing left censored data 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 RSS Show Notes   The post SOR 987 Dealing with Missing Data appeared first on Accendo Reliability.