Your Reliability Engineering Professional Development Site
Some products and systems are just dangerous, inherently. Understanding inherent safety-related risks permits a team to design out or mitigate those risks. Hazard analysis is the systematic process of identifying and controlling safety risks.
[Read more…]
This is part of a short series on the common life data distributions.
The Birnbaum-Saunders distribution is a univariate continuous distribution. This short article focuses on 7 formulas of the Birnbaum-Saunders Distribution. This distribution was designed to model the Miner’s rule, thus allowing for non-constant fatigue cycles through accumulated damage.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Beta distribution is a univariate continuous distribution. This short article focuses on 7 formulas of the Beta Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
The Beta function is not used to describe life data very often yet is used to describe model parameters that are contained within an interval. For example given a probability parameter constrained from 0 ≤ p ≤ 1 the use of the Beta distribution is well suited to model such a parameter.
The Beta distribution is also known as a Pearson Type I distribution. [Read more…]
This is part of a short series on the common life data distributions.
The Logistic distribution is univariate continuous distribution. This short article focuses on 7 formulas of the Logistic Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
This is part of a short series on the common life data distributions.
The Normal distribution is a continuous distribution widely taught. It is commonly used to describe items, measurements, or time to failure data when there are many additive perturbations that comprise the results. This short article focuses on 7 formulas of the Normal Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
Reliability engineering includes delivering bad news. This piece of equipment will fail soon, this design won’t survive outdoor use.
We start early with engineering judgment on design weaknesses. Continue by organizing groups to evaluate and comment on what will likely fail. We test, prod, poke and force failures to occur. Then we tally the actual performance and compare that to the what we hoped.
We are the bearers of bad news all too often.
So how do you avoid the stigma attached to that bad news? [Read more…]
This is part of a short series on the common life data distributions.
The Lognormal distribution is a versatile and continuous distribution. It is similar to the Weibull in flexibility with just slightly fatter tails in most circumstances. It is commonly used to describe time to repair behavior. This short article focuses on 7 formulas of the Lognormal Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
Engineers solve problems. We optimize solutions.
Engineering starts with a question. The work of engineering is answering those questions. Can we create an antenna with enough range? How can we make a safe autonomous driving car? How much can a delivery drone carry if it has a range of 100 miles?
Reliability engineers are no different. We ask questions and work to answer them. To solve the problems in the pursuit of providing our customers reliable solutions.
In general, there are only a few types of questions a reliability engineer addresses: What will fail, when, and what is the impact of a failure.
The answers are used to design reliable products, optimize supply chains and assembly processes, refine warranty accruals, and identify significant business risks. [Read more…]
I define a reliability failure as something that costs your organization money. It might be a warranty expense or a lost customer. A failure may also create customer expenses, too. Let’s talk about the range of un-reliability costs and their impacts.
[Read more…]
This is part of a short series on the common life data distributions.
The Exponential distribution is popular and useful in isolated situations. It has some nice features and flexibility that support it’s popularity. This short article focuses on 7 formulas of the Exponential Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
Fred and Philip Sage discussing the possible source of the widespread use of MTBF.
ᐅ Play Episode
This is part of a short series on the common life data distributions.
The Gamma distribution is routinely used to describe systems undergoing sequences of events or shocks which lead to eventual failure. Also used to describe renewal processes. This short article focuses on 7 formulas of the Gamma Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
There are many reasons or motivations to learn. From our boss asking us to solve a problem in an unfamiliar field of science, to simple curiosity.
When faced with an unusual failure mode, we need to learn what is causing the failure in order to solve the problem. When exploring a new material, we want to learn how it will fail in our design.
As reliability professionals, we are professional learners or should be.
Let’s take a look at a list of motivations that you may experience that prompt you to learn. When you review the past month or year, you will notice how much you learned.
When you feel one of these motivations, go with it. Learn, grow, and improve your capability as a reliability professional. Furthermore, you can foster these motivations with your team and colleagues, as well. [Read more…]
This is part of a short series on the common life data distributions.
The Weibull distribution is both popular and useful. It has some nice features and flexibility that support its popularity. This short article focuses on 7 formulas of the Weibull Distribution.
If you want to know more about fitting a set of data to a distribution, well that is in another article.
It has the essential formulas that you may find useful when answering specific questions. Knowing a distribution’s set of parameters does provide, along with the right formulas, a quick means to answer a wide range of reliability related questions. [Read more…]
The ability to determine the root cause of a failure is a skill. It requires agility, leadership, inquisitiveness, diligence, and technical skill. Successful RCA taps into your creativity and technical prowess. Using a structured approach helps.
[Read more…]
