Your Reliability Engineering Professional Development Site
Find all articles across all article series listed in reverse chronological order.
A simple assumption in many experiments is to assume the variable act independently on the response.
This means when I change the temperature a little in a polymer dryer silo that time to achieve a certain dryness goes down. And, changing the humidity or airflow rate or pressure either do not change or as they change has no impact on the relationship between temperature and drying time. [Read more…]
The purchase price of a product may be the start of the cost of ownership.
Maintenance and repairs may cost many times the initial purchase price. An efficient maintenance process and low-cost spare parts may help minimize cost, yet a design that optimizes the total cost of ownership is the most effective method. [Read more…]
Coupled with probability is the duration over which the probability applies.
For example, if we desire 99 of 100 to survive, we must state over which period of time this applies. It is proper to state the couplet of 99% reliable over 1 year.
It is not sufficient to define reliability as ‘5-year product’ as it does not contain the information related to how many are expected to survive the 5 years. Likewise, it is not sufficient to say a product has 5 – 9’s reliability (meaning the probability of failure is less than 0.00001) as it does not contain the duration.
If the product has high reliability for only a few seconds, that does not help us make judgments about the first year of life. [Read more…]
Dr. Nelson created a simple experiment to illustrate the effect of meddling with a stable process. (Dr. Deming did something similar)
While the intent of making small changes to a process may be to make improvements, these perturbations generally increase the variation of the results.
To improve a process requires understanding the source of the variation and often controlled experiments to identify process improvements. [Read more…]
For those that sat for the exam last Saturday – how did it go? what surprised you or confused you? Was your preparation adequate?
Cheers,
Fred
Roll the dice.
It is about that simple if any one product will survive to a specific time. Every product has a chance, not a guarantee. The time to failure for each product is a function of the use, stresses, assembly, latent defects or imperfections, and many other variables.
The result is generally unknown. And, we often establish a reliability goal that includes the probability of success. Keep in mind that a probability is only meaningful when defined over a specific duration.
Another variation of the X-bar and R chart, in this case measuring and plotting individual readings instead of a sample average. The range value is obtained from the current reading and a fixed number of previous readings.
This type of control chart is suitable for calibration or testing situations where it is not practical to create subgroups of items for samples. [Read more…]
This element of a reliability requirement answers the questions of where and under what conditions the product should operate.
It includes storage, transportation, and installation conditions too. One way to think of the environment is to consider the weather around the device. Temperature, humidity, preoccupation, etc.
How a customer uses a product matters. It matters in the amount and type of stress your product receives. It determines the life span. Someone that uses the product often isn’t necessarily going to have a short life span, it might be the lack of use that most damages a product.
The s chart replaces the R chart and provides an increase in sensitivity to variation of the spread of the data.
The s-chart works better with 10 or more items per sample in order to obtain the s (standard deviation) estimate. The use of a spreadsheet or calculator expedites the calculation of the sample standard deviation.
Control charts provide an ongoing statistical test to determine if the recent set of readings represents convincing evidence that a process has changed or not from an established stable average.
The test also checks the sample to sample variation to determine if the variation is within the established stable range. A stable process is predictable and a control chart provides the evidence that a process is stable or not.
Some control charts use a sample of items for each measurement. The sample average values tend to be normally distributed allowing straightforward construction and interpretation of the control charts. The center line of a chart is the process average. The control limits are generally set at plus or minus three standard deviations (of the sample means – commonly called the sampling error of the mean) from the grand average.
We need to understand what a product should do when working to be able to detect when it has failed.
When a function does not perform as expected that is a failure. Being very clear about an item’s function(s) is vital when establishing reliability goals. [Read more…]
Every process should operate stably. Every process may have many measurements available to monitor either various aspects to the final product or the assembly equipment. There may be hundreds of possible items to measure and monitor.
We do not have the resources nor time to apply control chart principles to each possible measurement. Control charts do not directly add value and they have a cost to maintain and interpret. While it may be tempting to add a dozen or so control charts, as the cost increases the value quickly decreases.
Once asked a customer what they wanted concerning product reliability.
She fully understood that some units will fail, that it’s matter of chance. She seemed understanding of the difficulty creating every product such that none would fail.
Then she confided that all that is fine, as long as the product she buys does not fail. [Read more…]
As stated before, variation happens.
The root cause of the variation for a stable process includes material, environmental, equipment, and so on, changes that occur during the process. No saw cuts the same length of material twice – look close enough there is some difference. [Read more…]
