Weibull Distribution Part2: Three-Parameter Weibull, B10 life, Characteristic Life

Dear viewers, we are happy to release this 26th video from Institute of Quality and Reliability! 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.

[@Doctor-Lean]

Thank you so much for sharing the VDO so, I request training apply concern with heuristic model if possible to sharing concept.

[@instituteofqualityandrelia7902]

Thank you for your suggestion. Kindly clarify your expectations.

[@catalystamlan]

May you live long to radiate the knowledge, benefitting generation to come

[@instituteofqualityandrelia7902]

Thank you so much Amlan for your wishes and appreciation!

[@TheSanthoshteja]

Thank you sir, I have been waiting for this

[@uhemant1]

Thanks. Appreciate your interest! Your feedback is welcome.

[@sunnyj1967]

Great Stuff. Cheers.

[@uhemant1]

Welcome! My pleasure!

[@asiftthottathil]

Thank you very much Sir for both part 1 and part 2 video of the Weibull Distribution. This video was very helpful to learn and know more about Weibull distribution.
Are log-normal distribution and Rayleigh distribution the same? Given a beta value of 2 at 5.51, it shows that it follows a log-normal distribution. But doesn't the resulting pdf indicate the Rayleigh distribution?

[@instituteofqualityandrelia7902]

Thanks for your question. It should be actually Rayleigh and not lognormal. I will clarify in the description. Apologise for the error and late response to your question.

[@M1urr]

I have a question about the failure-free time. In the video you mention batteries, tires, roller bearings... (see 7:31). Is there a scientific publication for one of the mentioned examples, in which it was physically justified why a failure-free time exists? Often a failure-free time is only confirmed by statistical analysis but I am looking for an example with a physical explanation. Do you know an example of this?
Thanks a lot and great video!

[@instituteofqualityandrelia7902]

Thanks for asking a very good question! Technically, there is always a "small" probability of failure till the "failure free life". However, in some cases, such as fatigue, failure is very unlikley at low number of cycles. So fatigue failure modes may have failure free life. A new tire is unlikely to fail due to wear. So for wear failure mode, there can be failure free life.
Hope this helps!

[@MrSaemichlaus]

Thanks for your presentation!

[@instituteofqualityandrelia7902]

Glad it was helpful!

[@sumitgharge1298]

Hello sir can you please tell me how we can find the values of shape parameter and scale parameter??

[@instituteofqualityandrelia7902]

Thank you! Please watch my video on how to estimate beta and eta parameters using probability plotting. Here is the link:
czcams.com/video/dsuLVS2yQ4U/video.htmlsi=i7saq3ej5S-wkYKk

[@sumitgharge1298]

@@instituteofqualityandrelia7902
Thank you very much sir..

[@ambersingh3280]

What if I want to calculate the MTTR using the weibull distribution and I also want to specify lower and upper boundaries..?

[@instituteofqualityandrelia7902]

Weibull Distribution is used when failure rate (and therefore MTTF) varies with time. So the upper and lower bounds may not be greatly useful as these are time dependent.

[@sudhansugrahacharya7094]

Sir weibul can represent almost all the distributions. Is it a good approach to consider weibul distribution in every data

[@instituteofqualityandrelia7902]

Weibull, Exponential and lognormal are the most commonly used distribution in reliability life data analysis. Normal is not very appropriate as it extends to -infinity while as failure time cannot be less than 1. Exponential is special case of Weibull with shape parametre of 1.

[@everestyoung5618]

Well, try to use the Weibull probability plot to find out whether that data's distribution represents the Weibull distribution.

[@sudhansugrahacharya7094]

What is the minimum sample size we should consider to determine the distribution of any data, considering the cost and other constraints.

[@instituteofqualityandrelia7902]

There are thumb rules suggested by experts. A suggested number is at least 20+. Of course, the larger the sample size, better it is but as you mention, cost and time constraints are important. For normal, it is generally 30+.

[@estevenofre4509]

Congrats!
I miss a video how to calculate Beta, Delta and Eta... to link those videos!
Regards!

[@instituteofqualityandrelia7902]

Do you need any clarification?

[@benghuatlaw1964]

Hi Hemant, may i seek your help, on how to estimate weibull shape parameter for 70% confidence (one sided LCL) by using chi square method

[@instituteofqualityandrelia7902]

Thanks. But I am unable to understand your question. Apologise.

[@benghuatlaw1964]

@@instituteofqualityandrelia7902 my question if we would like to apply one sided confidence interval for weibull shape parameter, is there a method?

[@dileepmathews85]

Hi Hemanth, is characteristic life same as the useful life of a product?

[@instituteofqualityandrelia7902]

Thanks for your question and keen interest. The characteristic life is NOT same as useful life! Characteristic Life is time by which 63.2% parts are expected to fail and ! Characteristic Life is used in Weibull Distribution mathematical equation and calculationns.

[@kumarvel2320]

Beta calculation method not addressed in the training

[@instituteofqualityandrelia7902]

Have u seen part-1?