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The following note and question appear in my email the other day. I had given the definition of reliability quite a bit of thought, yet have not really thought too much about a definition of ‘product life time’.
So after answering Najib’s question I thought it may make a good conversation starter here. Give it a quite read, and add how you would answer the questions Najib poses. [Read more…]
Ernst Hjalmar Waloddi Weibull (18 June 1887 – 12 October 1979) was a Swedish engineer, scientist, and mathematician. (source Wikipedia)
In days of yore, systems development projects were front ended with laborious requirements engineering and design tasks. This made sense then because development was labor-intensive, time-consuming and expensive. Changes to the scope or design of a solution mid-development increased the likelihood of errors and incremental time and expense. In recognition of this, traditional Waterfall project management was applied, which created impediments to modifying the product definition once its development had begun. Changes were strenuously resisted.
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When you run machines above design rates that decision goes against all that we know about creating high plant uptime and outstanding equipment reliability—in fatigue situations 10% additional stress will cost you ten breakdowns.
The dominate factor in machine life and production plant uptime is the stress in your machines’ working parts. The stress developed in a part’s material of construction microstructure is directly linked to the force applied to it. It does not matter where the force comes from or why it is applied, once the stress in your parts go beyond their microstructure limits your machines fail. If you want to run at high production rates first ensure that your working parts cannot become overstressed.
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Dear viewers, I am happy to release this second video on Accelerated Life Testing. In this video, we have discussed (i) Various models used to estimate the acceleration factor, such Arrhenius, Power law etc. (ii) Application examples showing calculation of acceleration factor (iii) Some mathematical relationships to estimate reliability at normal operating conditions and illustrated examples (iv) Types of stress loading used in ALT.
The video will be useful to all those who want to learn ALT and its mathematical relationships. It would be of great help to those who wish to take ASQ CRE certification exam. It would also be useful to DFSS practitioners.
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We are back with our 4th installment in the Opportunities for Maintenance and Operations series! We have discussed alarms, rework, and event rates. Today we talk about PM Routes. What can a PM Route do if our CMMS system has the ability to manage them? How does one take advantage? Watch the video and find out.
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Leaders and managers play many roles: planning, scheduling, coaching, teaching, supervising, hiring, and sometimes firing. While much of this work is routine, it often involves making decisions. Some decisions are low-risk with clear facts and limited options. Others are made with murky details, unknown options, and high risks if incorrect. Regardless, decision-making often falls to leaders and managers. Theodore Roosevelt, the 26th President of the United States, famously said: “In any moment of decision, the best thing you can do is the right thing, the next best thing is the wrong thing, and the worst thing you can do is nothing.”
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In probability theory and statistics, the Weibull distribution is a continuous probability distribution. It models a broad range of random variables. Largely in the nature of a time to failure or time between events. It addresses mechanical or structural failures in the field of Reliability Engineering. By nature, the Weibull distribution provides a lot of information such as aging characteristics or expected asset lifetime. One of its most common outputs is the Bathtub Curve.
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For thousands of years, doctors treated virtually every skin ailment by ‘letting’ or draining the blood of the patient. Leeches are really good at doing this as they quite literally drink up the allegedly ‘poisoned’ blood that is being removed. Of course, by the late 1800s, science had advanced to the point where it was realized that this was nonsense, and so leeches fell out of favour in the world of medicine.
But that same scientific revolution saw the development of drugs like heroin and cocaine to cure everything from schizophrenia through to children’s cough. With doctors prescribing these drugs left right and centre, and worldwide epidemic of drug-addiction misery was spawned.
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We often jump to the wrong conclusions when non-compliance occurs. Non-compliance occurs when the standards and regulations designed to protect public health and the environment are unmet. Obviously, we must reduce immediate risks. However, these measures often address the symptoms and not the root causes. This leads to further delays or doubling down on the wrong things as we move through mitigation that misses the mark. Using statistical process control in environmental root cause analysis is one way to make sure you’re getting it right!
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Here’s a common problem: You have been tasked with peering into the future to predict when the next failure will occur.
Predictions are tough.
One way to approach this problem is to analyze the history of failures of the most typical system. The issue looms larger when you have only two observed failures from the population of systems in question.
While you can fit a straight line to two failures and account for all the systems that operated without failure, it is not very satisfactory. It is at best a crude estimate.
Let’s not consider calculating MTBF. That would not provide useful information as regular readers already know. So what can you do given just two failures to create a meaningful estimate of future failures? Let’s explore a couple of options. [Read more…]
Having a knowledge of how the data is distributed is critical to model failure times and life in reliability analysis. Every distribution is unique and suitable for different types of reliability data.
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The history of Design of Experiments (D.O.E) can be traced back to the work of various individuals, including Genichi Taguchi, a Japanese engineer and statistician. Taguchi made significant contributions to the field, particularly in the area of robust design, which aimed to improve the quality of products and processes. His work was influenced by the need for quality improvement in post-World War II Japan. Taguchi’s methodology, known as the Taguchi methods, was based on the concept of “robust parameter design,” which aimed to make processes and products insensitive to environmental factors or other variables that were difficult to control.
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Physics-of-Failure warns us to keep the sum of all static and cyclic loads on a part’s microstructure below its fatigue stress limit.
The image below shows two example metal fatigue limit failure curves. These curves were determined based on controlled laboratory experiments. These experiments use a machine with a fixed load to test the selected piece’s microstructure stress levels. Curve A shows that at a high stress level, close to the Ultimate Tensile Strength of steel, the test piece failed after 10,000 cycles. As the fatigue stress level is reduced, the test piece lasts longer. When the imposed stress is limited to around 50% of the UTS, the cycles to failure had no measurable limit. On the other hand, Curve B shows that at all levels of fatigue stress the component would eventually fail. However, the same outcome can be seen, that as stress reduces the service lifetime before failure increases.
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Black Lives Matter is a slogan that we see at major sporting events, and on social media, however it is a fact that female managers of color are five times more likely (21%) than their white female counterparts (4%) to say they have quit a job after being overlooked for a new leadership opportunity at work.
While it’s not really news that women and especially women of color are more likely to encounter roadblocks in the journey to a leadership role, it’s very disconcerting to how it’s affecting not just the women, but the companies that are losing out on this talent.
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