Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
Normal distribution is most common in real life scenarios be it modeling any reliability performance parameter at a specific time. Central Limit Theorem (CLT) shows why normal distribution occurs so often.
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When I was a bright eyed, motivated (younger) officer in the Australian Army, one my many tasks when deployed overseas was to raise paperwork to formally request ‘battlefield material’ to be sent back home from whatever country we were in. ‘Battlefield material’ was items that included a range of mementos, keepsakes, and things you would typically see in a museum to add to the historical collections of my battalions and regiments back home.
Ernst Hjalmar Waloddi Weibull (18 June 1887 – 12 October 1979) was a Swedish engineer, scientist, and mathematician. (source Wikipedia)
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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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.
[Read more…]It is no small irony that a software application that is designed to protect IT systems from malicious actors was behind the biggest IT outage in the history of computers. A company called Crowdstrike provides a ‘Falcon Sensor’ product that is intended to scan computers that use Microsoft operating systems for vulnerabilities. And this product is deployed so deeply into its host operating systems that it has access to the ‘kernel,’ which is the program that runs the basic code that links applications to the computer hardware (like memory, central processing unit and other devices). Unfortunately a Falcon Sensor update that Crowdstrike sent to its customers had a bug that was not picked up by its own validation programs (because it too had a bug). And unfortunately, it accesses a ‘forbidden’ part of the memory that causes the infamous BSOD or ‘blue screen of death.’ So airlines, hospitals, banks, hotels and lots of other companies simply couldn’t operate.
And again there is no one answer for such simple question. Strongly depends on what type of test you need to conduct.
It is also very important to understand at which stage, design of the product. Usually at very early stages of the design there is many unexpected failures, when design is mature enough – failures become predictable, and there is one last period, called – wear out/aging stage.
[Read more…]How heavy should you be? Perhaps this is a sensitive question. The average weight of a human is about 65.2 kg or 143.7 lbs. So if your weight is above this figure, are you ‘too heavy’? Conversely, if you are below this figure, are you ‘too light’? Being over and underweight can bring a whole raft of health consequences.
Hopefully you would agree with me in saying that the ‘average’ human weight is not a good benchmark to use if you want to get healthier. Or at least it is not the only benchmark you should think about.
But unfortunately … many manufacturers use approaches that are embarrassingly close to this ridiculous approach to continual improvement.
The 8D (Eight Disciplines) method is a problem-solving methodology designed to find the root cause of a problem, devise a short-term fix, and implement a long-term solution to prevent recurring problems. It was first introduced in Ford’s 1987 Team-Oriented Problem Solving manual and has since become a widely used problem-solving method, also known as Global 8D. The 8D process consists of eight disciplines, each focusing on specific aspects of problem-solving
[Read more…]I am constantly confronted by students, reliability engineers and other people banging fists on tables and saying …
… 89 percent of failures are random …
Firstly, 100 percent of failures are random. It’s just that there are lots of textbooks and experts telling us that a ‘random’ failure is one that happens irrespective of age. That is, a failure with a constant ‘failure rate’ where the item in question doesn’t appear to age or wear out.
Statistical distribution – it has important role in general life and of course in engineering specifically. So, what is all about?
During morning commute either by bus or vehicle, you take some budget of time. From your daily routine experience, you with very high probability, can predict arrival time. You also know how much time it will take, in less ordinary cases, like slippery roads, or cars accident along you route.
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A short introduction to accelerated life testing or ALT. It is a method used to enhance product reliability by subjecting prototypes to stress levels significantly higher than those encountered in actual use. The idea is to rapidly induce failures which is equivalent to speeding up time.
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Anybody who did some hardware test in they life, eventually will face the question of sample size.
Probably it will be a tradeoff between the test duration and amount of samples to test.
So how much is enough? One, three, ten?
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In this article, I want to show you why Weibull, lognormal, and exponential distributions are more appropriate than others for modeling failure distributions. Instead of starting from the explanation of the most used fitting distributions in reliability engineering, I will begin with the analysis of artificial datasets created assuming specific failure behaviors. In other words, I will start from the data and not from the statistical distributions. I think that this approach can help those who deal with this topic for the first time.
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