Your Reliability Engineering Professional Development Site
When looking at a pile of data, sometimes there is a data point that is not like the others. It attracts attention as it is different than the rest of the data.
When I spot something odd in a dataset, I wonder if there is something to learn here. Is this an opportunity to make a discovery or improve a process?
All too often it is tempting to remove the outlier as a mistake. Or to drop the outlier as it doesn’t make any sense and ‘messes up’ the analysis. [Read more…]
There is a lot of probability, statistics and data analysis involved with reliability engineering. Why is that? Have you considered why our field of endeavor includes the use of these tools?
Let’s say there were no statistical tools. We would not be able to accurately infer a conclusion based on an observation of a few samples. We might react to everything that we observe – constantly spinning our wheels on minor issues. We might make decisions based on factors that did not include the random variation of the items. We might track failure rates, yet not really know how to determine if the few failures we observe were an indication of a major issue or not. [Read more…]
You use your calculator or spreadsheet, or even a statistics software package to calculate standard deviation, which is an estimate of the population standard deviation. Yet, understanding how one could calculate standard deviation without such advanced tools may prove useful. The knowledge of basic sum of squares methods provides a foundation for ANOVA and DOE analysis techniques. [Read more…]
When time is short and you just want a rough estimate of the standard deviation, turn to the range rule to quickly estimate the standard deviation value.
The standard deviation is approximately equal to the range of the data divided by 4. That’s it, simple. [Read more…]
In statistics and reliability, we use distributions to describe time to failure patterns. The four functions commonly used in reliability engineering include
We often use terms like, mean, variance, skewness, and kurtosis to describe distributions (along with shape, scale, and location). The mean is defined as the use of a moment generating function. First though let’s first back up to the concept of center of gravity (cog) from mechanics. [Read more…]
Recently a question came to me about the difference between hazard rate and failure rate. A lot of literature and reference use these two terms interchangeably. I have long thought there is a difference, and to check my understanding I asked a good friend and reliability engineering consultant Chet Haibel. He provides a slide set (below) that described the various functions we use working reliability statistics and he details out the functions when using the exponential distribution.
Please review the slides and comment or ask questions – both Chet and I will attempt to help clarify any misunderstandings. I do think the slides are excellent and will do a great job providing a review of basic reliability math and the exponential distribution.
Related:
Using The Exponential Distribution Reliability Function (article)
Basic Statistics (article)
The Exponential Distribution (article)
As with the Markov Inequality, we may find useful information from a list of values, say time to failure data. Again, none of the numbers may be negative for this to apply, yet with life data that is rarely the case.
Short on time- a common situation for reliability engineers; we have only the mean, standard deviation and number of values in a list. And, we need to say something about the data and the number or fraction of value above a specific value. [Read more…]
If we have a list of numbers, saw cycles to failure for a test. None are negative. And, we do not have time for a complete analysis before being asked about the results.
What can we do?
In this case, the Markov Inequality may prove useful for a quick assessment of the results. [Read more…]
A foundational element of probability and statistics is counting. How many ways could something occur? A simple example is a pass or fail criteria, thus when evaluating a product there are two possible outcomes. [Read more…]