Life Data Analysis with only 2 Failures

Life Data Analysis with Only 2 Failures

Here’s a common problem: You have been tasked with peering into the future to predict when the next failure will occur.

Predictions are tough.

One way to approach this problem is to analyze the history of failures of the most typical system. The issue looms larger when you have only two observed failures from the population of systems in question.

While you can fit a straight line to two failures and account for all the systems that operated without failure, it is not very satisfactory. It is at best a crude estimate.

Let’s not consider calculating MTBF. That would not provide useful information as regular readers already know. So what can you do given just two failures to create a meaningful estimate of future failures? Let’s explore a couple of options. [Read more…]

by Fred Schenkelberg 13 Comments

The Business of Providing MTBF

What Price Providing MTBF?

What good is your service if your livelihood consists of providing MTBF upon request?

Sure, you earn some money, yet did the customer receive value in the transaction? As you know, or should know, MTBF is so commonly misunderstood that the customer is likely to be confused about what they want, reliability or MTBF. Providing them with MTBF does not answer their question.

Worse, the customer thinks they got something of value and blithely heads off with rather meaningless information.

My contention is to provide MTBF because the customer’s request is wrong. We know better. Those performing predictions, doing data analysis, and other reliability engineering work know that MTBF is a faulty and rather meaningless metric often confused with reliability, R(t). (probability of success over a duration). [Read more…]

by Fred Schenkelberg Leave a Comment

The Many Ways of Data Analysis

Given Some Data, Do Data Analysis

Let’s say we have a set of numbers, {2.3, 4.2, 7.1, 7.6, 8.2, 8.4, 8.7, 8.9, 9.0, 9.1} and that is all we have at the moment.

How many ways could you analyze this set of numbers? We could plot it a few different ways, from a dot plot, stem-and-leaf plot, histogram, probability density plot, and probably a few other ways as well. We could calculate a few statistics about the dataset, such as mean, median, standard deviation, skewness, kurtosis, and so on. [Read more…]

by Fred Schenkelberg Leave a Comment

High MTBF with Low Reliability

Can You Have a High MTBF and Low Reliability?

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…]

by Fred Schenkelberg 18 Comments

How About Weibull Instead of MTBF?

What About Weibull? Can I Use it Instead of MTBF?

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…]

by James Kovacevic Leave a Comment

What Can You Do With Data?

A Question & Answer Period with Fred Schenkelberg and James Kovacevic on what can be done with your data and analysis.

Data and the analyses that use the data can be tricky to manage at best, let along extremely difficult.

In this last post of the series on using the maintenance data you have, Fred and James will answer many of the common questions asked about data and the analyses. [Read more…]

by Dennis Craggs Leave a Comment

A Primer on Probability Distributions

The most common types of engineering data are measurements. There can be a few, thousands, or millions of data points to analyze. Without analytic tools, one can get lost in the data.

This article presents

Dotplots
Data if frequently clustered about a central value and displays variation.
Frequency histograms
Distribution characteristics
Normal Distributions

[Read more…]

by Fred Schenkelberg 9 Comments

Field Data and Reliability

Customers experience product failures.

Understanding these failures that occur in the hands of customers is an essential undertaking. We need this information to identify increasing failure rates, component batch or assembly errors, or design mistakes. [Read more…]

by Fred Schenkelberg Leave a Comment

The Music of Data

We are good at collecting data, now use it

In Katmandu, I visited a small pottery factory. There was a young man sitting at a potting wheel making candle stands. He didn’t measure anything and I doubt anyone did.

Based on experience and just looking at a finished item, he could tell if it was acceptable or not. That was good enough.

[Read more…]

by Fred Schenkelberg Leave a Comment