Your Reliability Engineering Professional Development Site
If you buy more than one of an item used in your product, you will have to deal with variability. In general, the variability from part to part is minimal and expected. Occasionally, the variability is large and causes reliability problems. [Read more…]
As with other point estimates, we often want to calculate the confidence interval about the estimate. The intent is to determine the range of reasonable values for the true and unknown population parameter. For MTBF, this no different.
It was late Friday afternoon and the phone rang. Which is rarely a good thing.
There seems to a significant spike in field failures due to one component. The initial failure analysis work reveals the issue started with a batch of parts received about two months ago and the flaw continues to appear in subsequent batches. [Read more…]
A common request of reliability professionals is to design a set of reliability tests.
Many rely on standards, such as 85°C / 85%RH, to determine if a system is reliable, yet the standards do not provide a means to translate the results to your specific situation.
When using a sample to calculate a statistic we are estimating a population parameter. It is just an estimate and the sample due to the nature of drawing a sample may not create a value (statistic) that is close to the actual value (parameter). [Read more…]
The communication between suppliers or vendors and their customers is often using a mix of specifications and requirements.
Customers set requirements and suppliers offer specifications. When they match, or when a supplier component specifications meet the customer’s requirements, we have the potential for a transaction. [Read more…]
Control charts help us monitor and stabilize a process. A little graphics along with statistics provides a tool to identify when something has changed. Some changes are abrupt and obvious, other a little more subtle, yet the out of control signals each have approximately the same chance of alerting us to a change.
A little graphics along with statistics provides a tool to identify when something has changed. Some changes are abrupt and obvious, other a little more subtle, yet the out of control signals each have approximately the same chance of alerting us to a change. [Read more…]
As you may recall the probability density function describes the behavior of a random variable.
Like a histogram, the PDF when plotted reveals the shape of the distribution. The PDF also has the property that the area under the curve for is one. Another property is the PDF is defined across the entire sample space. [Read more…]
“Keeping the end in mind”, “working toward a common objective” and “providing a vision” are all convention management wisdom based on setting goals.
Seeing a reliability goal is one of the first tasks when creating a reliability plan.
“How good (reliable) does it have to be?”
That is answered with a reliability goal statement.
There is a lot of uncertainty concerning a reliability goal. [Read more…]
It seems that anytime we draw a sample, it should be taken randomly. Statistics books and papers regularly advise using a random sample. The adverse effect on results drawn from the experiment may hinge on the randomness of the selection of samples. [Read more…]
My introduction to reliability engineering was my boss asking me to sort out how long a new product will last in use.
The expectation was it would last for 20 years or more buried in Italian mountain concrete bridges.
My first thought was about living in the Dolomites for 20 years monitoring the performance of the product.
That was quickly dashed as my boss explained he wanted an answer in about 6 months.
Now this was a problem. How do you cheat time to learn about the expected lifetime of a something? Thus started my career in reliability engineering.
Life testing for reliability engineering helps us answer the question how long till failure occurs. Specifically, we find the chance of failure over some duration. [Read more…]
When components fall outside specs, the chance for quality and reliability issues increases.
Let’s explore some design and manufacturing cases where effectively using statistical process control will enhance your product’s reliability performance.
This nonparametric test evaluates if two continuous cumulative distributions are significantly different or not.
For example, if the assumption is two production lines producing the same product create the same resulting dimensions, comparing a set of samples from each line may reveal if that hypothesis is true or not.
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…]
If nothing was uncertain we would not need statistics.
Since nearly everything varies in some fashion, we need a way to describe and work with that variability.
We already know this and we know about statistics as being the right set of tools. Yet we hesitate, avoid, and refuse to pick up the appropriate tool. [Read more…]