Let's explore where the data comes from and how to prepare for analysis. Plus, let's discuss some ways to look at your data initially.

We have data. Often, an abundance of data concerning equipment failures. Failures per month or MTBF-type measures do not reveal sufficient insights to understand the pattern of failures.
We need to know if the rate of failures is increasing or not and if the maintenance program is helping or hurting the equipment long term. We must understand the pattern of failures to align our maintenance strategy properly.
Let's explore two ways to use the time to failure data you already have available (or should have). For repairable items, the mean cumulative function and associated plots provide you with an estimate of the effectiveness of your repairs. Are repairs restoring the system to good-as-new' condition, bad-as-old', or somewhere in between?
This Accendo Reliability webinar originally broadcast on 11 October 2016.
To view the recorded webinar and slides, visit the webinar page.
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Is Maintainability Only About Repair Time? episode
Let's explore where the data comes from and how to prepare for analysis. Plus, let's discuss some ways to look at your data initially.
For repairable items, the mean cumulative function and associated plots provide you with an estimate of the effectiveness of your repairs.
We will discuss the pros and cons of various sources. Plus, let's examine a few ways to use simulations or models.
The Weibull distribution is a versatile tool to analyze time to failure data. Like any tool, it could be wielded well or not so well.
The design is done, the assembly process is working, now we can focus on answering the question: is the product hitting reliability targets?
Data is only as useful as the information you derive. So would you like to take your Weibull probability plotting skills to the next level?
Minitab itself has many reliability functions available; this presentation covers the basics, including distributions, censoring, and fitting.
This webinar examines an important perspective. Its' so simple and has made many heroes in the data analysis world since Abraham Ward.
Some of you may have heard of Bayesian analysis.' You may think this is something fancy that only universities do.
Let's take a closer look at the concept of likelihood and it's role in an MCMC analysis. A powerful tool for data analysis.
This webinar is about how we use this thing called Markov Chain Monte Carlo Simulation (MCMC) to create this posse.'
We show you how to get your computer to help you give useful reliability information to your boss, manager, director, or whoever.
To create test results that are meaningful, we need to both design and execute the test well, then, interpret the results accurately.
there are ways you can suck out information from a group of experts in a quantifiable and remarkably accurate way.
A Weibull plot is a really useful way of quickly looking' at data and being able to see' really useful things.
WeiBayes is useful, and there are quite a few catches. Interested in learning about Weibayes analysis? Join us for this webinar.
Sometimes the equations we need to model reliability are just so complicated that we simply avoid them. Let's use Monte Carlo instead.
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