Weibull Distribution Archives - Accendo Reliability

Reliability Engineering using Python

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.

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by Fred Schenkelberg 5 Comments

The 2 Parameter Weibull Distribution 7 Formulas

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…]

by Fred Schenkelberg Leave a Comment

How to Calculate Reliability Given 3 Different Distributions

On occasion, we want to estimate the reliability of an item at a specific time.

Maybe we are considering extending the warranty period, for example, and want to know the probability of no failures over one year instead of over the current 3 months.

Or, let’s say you talked to a bearing vendor and have the Weibull parameters and wish to know the reliability value over 2 years.

Whatever specific situation, you have the life distributions parameters. You just need to calculate reliability at a specific time. We can do that and let’s try it with three distributions using their respective reliability functions: exponential, Weibull, and lognormal. [Read more…]

by Fred Schenkelberg 5 Comments

Field Data Analysis First Look

Field data analysis starts with the collection of data.

In a previous article, we used a Nevada chart to gather the counts per month of field failure data. The chart also provides the necessary data to account for how many units have not failed as of yet.

The Nevada chart on its own is just a table of numbers and does not reveal patterns of the changing nature of failure rates over time. Are we experiencing early life failures or wear-out related failures?

We need to conduct some data analysis to learn what message the data contains. [Read more…]

by Fred Schenkelberg 2 Comments

Weibull Distribution

A continuous distribution is useful for modeling time to failure data. For reliability practitioners, the Weibull distribution is a versatile and powerful tool. I often fit a Weibull when first confronted with a life dataset, as it provides a reasonable fit given the flexibility provided by the distributions parameters. [Read more…]