Your Reliability Engineering Professional Development Site
Underpinning the coherence of statistical process control, process capability analysis and numerous other statistical applications is a phenomenon found throughout nature, the social sciences, athletics, academics and more. That is, the normal distribution, or less formally, the bell curve. Because of its ubiquity, this normal distribution is arguably the most important data model analysts, engineers, or quality professionals will learn.
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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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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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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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This is the second part of our two videos on Weibull Distribution. In this video, Hemant Urdhwareshe discusses few more concepts of Weibull Distribution, such as characteristic life, three-parameter Weibull and B10 life for Weibull Distribution. The concepts are explained with an application case study to calculate reliability using the Weibull Reliability function. The video would be useful to all those who want to learn applicability of Weibull Distribution to estimate reliability and to those who wish to take ASQ CRE, CQE, Six Sigma Black Belt and CMBB certification exams. In Weibull Distribution Part-1, Hemant had explained various basic concepts in Weibull Distribution with brief discussion on mathematical relationships.
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As regular readers know, MTBF by itself is misleading. It can also be deceptive when representing actual data. Just because you have a high MTBF value doesn’t mean it is reliable.
In a previous article, 10 Reasons to Avoid MTBF, I mentioned that it is possible to have a relatively high MTBF value when the actual reliability is low. Ashley sent me the following note:
Hi Fred, i love reading your articles they are very informative. I have a question about something you said in a comment which i am hoping you will be able to clarify for me. You said products with higher MTBF can actually be less reliable than products with a lower MTBF
I have tried to find information on how this is possible online, and tried to do the maths myself to make this happen but i have to admit i am struggling.
No worries, Ashley, let’s work out an example to illustrate what I meant. [Read more…]
This is the first of our two videos on Weibull Distribution. In this video, Hemant Urdhwareshe explains the basic concepts of Weibull Distribution, its cumulative and probability functions, hazard function and effect of shape parameter on these functions. Hemant also explains the related mathematical treatment briefly. The concepts are explained with an application case study of tires to calculate reliability within warranty period. The video additionally explains calculating probability of failure and reliability using Microsoft Excel functions. The videos would be useful to those who want to learn applicability of Weibull Distribution to estimate reliability and to those who wish to take ASQ CRE, CQE and CMBB certification exams.
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This was a follow-up question in a recent discussion with Alaa concerning using a metric other than MTBF.
The term ‘Weibull’ in some ways has become a synonym for reliability. Weibull analysis = life data (or reliability) analysis. The Weibull distribution has the capability to describe a changing failure rate, which is lacking when using just MTBF. Yet, is it suitable to use ‘Weibull’ as a metric? [Read more…]
In this video, Hemant Urdhwareshe explains concept of hazard rate which is very important in reliability engineering and also in insurance business! The video will be useful to all those who want to understand this basic concept. It will be also be useful to all those who wish to prepare for ASQ Certified Reliability Engineer (CRE) exam.
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In this video, Hemant Urdhwareshe explains applicability of Poisson Distribution along with its application examples. Solutions to application examples are illustrated using basic formula, tables of Poisson Distribution and also Microsoft Excel. Hemant also explains the mean and standard deviation of Poisson Distribution. The video will be useful to all those who want to learn basics of Poisson Distribution and prepare for ASQ Certification Exams such as Certified Quality Engineer (CQE), Certified Reliability Engineer (CRE), Six Sigma Black or Green Belt (CSSBB or CSSGB).
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Dear friends, we are happy to release this video on Binomial Distribution which is one of the foundations in probability theory! In this video Hemant Urdhwareshe explains how to calculate probability using basic formula, tables and Excel. We have also explained the applicability of Binomial Distribution in various situations. Additionally, the video includes graphical representation of Binomial Distribution using Excel. The video would be useful to all those who want to learn the Binomial Distribution and take the ASQ Certification exams such as CQE, CRE, Six Sigma Black/Green Belt.
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Institute of Quality and Reliability is happy to release this video to understand chi-square distribution. In this video, Hemant Urdhwareshe explains the chi-square distribution with a simulation to understand it better. Chi-square is one of the basic distributions used in hypothesis tests. The video is limited to understanding of the distribution and explains how to use tables of chi-square distribution. The video will be useful for professionals who want to take the ASQ Certification exams such as CQE, Six SIgma Black or Green Belt, CMQ/OE, CRE etc.
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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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Normal distribution is one of the foundation concepts in statistics and every quality and reliability professional must understand and able to apply this concept. There are already may videos on this subject, but Hemant Urdhwareshe has tried to make the concept as simple as possible in this video. Hemant has explained the history and basics of normal distribution and its properties. Hemant has further explained the concept of standard normal distribution and how to apply it to solve some typical problems using tables of standard normal distribution. You can download the tables from www.world-class-quality.com.
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Software tools are a cornerstone of modern Reliability Engineering, enabling reliability practitioners to perform their analysis without getting bogged down in the details of the underlying mathematical processes. There are many software tools available for reliability engineering, some of which are tailored to this application, while others are more general statistical tools which can be adapted to the needs of reliability engineers. One thing these tools have in common is their graphical user interface (GUI). The GUI requires only a basic level of knowledge to operate, but with a few clicks of the correct buttons, the desired task can be achieved with relatively little mental effort. It is the user friendly GUI that draws reliability engineers to select such applications as their tools of choice for performing reliability engineering analyses.
