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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…]
There are a lot of reliability tools.
From FMEA to FTA, from ALT to HALT, from derating to sneak circuit analysis. We also have a lot of acronyms. We cannot afford to do all the tasks, so which do we select and why?
Each activity has some reason for existing. Each has some question that it helps answer. HALT helps to find what will fail. ALT helps to determine when failures may occur.
Knowing what each tool is capable of doing is a start. Knowing what you need to know is essential.
An easy method to monitor and control a process average. It is an alternative to the Shewhart control chart.
Pre-control charts work well with stable and slow process drifts or changes. These charts provide a means to monitor a process and act as a guide for process centering.
They are easier to setup, implement, and interpret the Shewhart charts. [Read more…]
Every failure provides information. It provides time to failure, stress strength relationship, process stability and design margin types of information. In every case. Even failures directly related to human error.
A hardware intermittent failure observed by a firmware engineer should not be dismissed. Rather recorded, explored and examined.
A single intermittent failure, or glitch, may indicate nothing other than just a totally random glitch, or a design error that degrades over time causing 50% of units to fail in first three months.
The connection between the specifications or drawings or design requirements and the manufacturing process is the capability of the process to consistently create items within spec.
A ratio of the specification over the spread of measured items provides a means to describe the process capability.
The ratios rely on the standard deviation or spread of the produced items. The index is meaningful only if the process is stable. Thus beyond making sure the measurements have minimal measurement error, check the stability using the appropriate control chart.
In this article we are assuming the measurements are normally distributed, yet knowing that is not always the case, you can calculate capability indices using the actual distribution.
The indices will have similar interpretations yet take care when applying these concepts using other than normal distribution data.
Some days are better than others. We sometimes run into failure when working to create a new product. With a little investigation we suspect the components are not working as expected.
We’ll call the vendor and ask for an explanation. If this is normal production and variability of performance, our product will suffer an higher than expected failure rate. The vendor will assure us with:
Not helpful. [Read more…]
The basic idea of reliability growth is the information learned during testing allows the team to make improvements.
The improvements then reveal themselves in the next round of testing. There are improvements during each test phase as the immediate fixes occur.
Plus some improvements may have longer lead times and be implemented in time for the next round of testing. [Read more…]
