Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
At my job interview, the new product development director, an econometrician, explained that he tried to forecast auto parts’ sales using regression. His model was
sales forecast = SUM[b(s)*n(t-s)] + noise; s=1,2,…,t,
where b(s) are regression coefficients to be estimated, n(t-s) are counts of vehicles of age t-s in the neighborhood of auto parts stores. The director admitted to regression analysis problems, because of autocorrelation among the n(t-s) vehicle counts, no pun intended.
Traditional SPC methods were developed to support high volume production and long production runs. However, with the trend toward product specialization, product diversity, and flexible manufacturing, short production runs have become more common. Applying SPC in the traditional manner presents challenges in short production runs, because by the time enough data is collected to establish valid control charts, the production run may be over! [Read more…]
In the last article, we learned how to determine the coefficients of a predictive model for 2-level screening designs. It is more complex to determine model coefficients for multi-level experiments so for those, we rely on statistical methods software.
In this article, we look at using the model to develop solutions. So that we learn the basics, we first use some simple algebra to find a solution. Then, in the next article, we will explore some common tools that are found in DOE software programs to help uncover solutions. [Read more…]
When processes trend naturally due to tool wear, traditional control charting methods fail. The trend (which is expected) results in inappropriate “out-of-control” signals. Control charts should detect unexpected changes in the process. If the trend is expected, we do not want to be alerted to this trend. If no accommodation is made for this trend, the chart will incorrectly produce “out-of-control” signals. [Read more…]
“If a man does not keep pace with his companions, perhaps it is because he hears a different drummer. Let him step to the music he hears, however measured or far away.” Henry David Thoreau
One of the most important skills in facilitating team meetings is to be able to encourage balanced participation by all team members.
A computer company tiger team held a meeting to decide how to fix their laser printer ghosting problem. Bearings seized in the squirrel-cage cooling fan for the fuser bar. The fan bearing was above fuser bar, which baked the bearing. A fix decision was made, voted on, and accepted. Party time. I asked, “How do you verify the fix?” Boo!
This an example of using current status life data. I checked status every laser printer laser-printer fan in company headquarters: operating or failed? Date of manufacture was encoded in the printer serial number, so I estimated the fan’s age-specific failure rate function, before the fix. Premature wearout was evident. Could I observe repaired or new printers at a later time and test the hypothesis that the problem had been fixed? Yes.
In the last article, we learned how to determine which effects are statistically significant. This is an important step to develop the predictive model(s) because only the statistically significant factors and interactions belong in the model. If we include insignificant terms in the model, the predictive ability of the model will appear to be better than it really is and we will overstate the ability of our model to predict the response(s). [Read more…]
In previous articles, we discussed the advantages that Xbar charts have over Individuals charts in detecting process shifts. (See “How Should the Subgroup Size be Selected for an Xbar Chart (Part I)”) We saw that charts of Individuals are ineffective at quickly detecting small process shifts (and detecting these small process changes may be critical!).
Charts of averages (Xbar) are superior because averaging the subgroup data gives us greater certainty as to where the process is actually running at a point in time. Selecting an appropriate sample size on an Xbar chart allows us to align the statistical performance of the control chart with the desired practical process changes we’d like to detect. That is, the sensitivity (ability to detect change) may be adjusted based on the sample size utilized. [Read more…]
In the last article, we learned how to compute and graphically interpret both main effects and interaction effects. Eventually, the statistically significant effects will be used to develop a predictive model. But how do we determine which effects are statistically significant?
Conceptually, we first develop an “error” distribution that represents the distribution of Insignificant Effects. If we have an idea of what the Insignificant Effects look like, we can determine which of the effects we compute look significant by comparison. [Read more…]
Software tools are a cornerstone of modern Reliability Engineering, enabling reliability practitioners to perform their analysis without getting bogged down in the details of the underlying mathematical processes. There are many software tools available for reliability engineering, some of which are tailored to this application, while others are more general statistical tools which can be adapted to the needs of reliability engineers. One thing these tools have in common is their graphical user interface (GUI). The GUI requires only a basic level of knowledge to operate, but with a few clicks of the correct buttons, the desired task can be achieved with relatively little mental effort. It is the user friendly GUI that draws reliability engineers to select such applications as their tools of choice for performing reliability engineering analyses.
When analyzing a continuous variable or type of measurement using statistics, an analyst often assumes data is normally distributed. But, how can this normal assumption be verified? While there are numerical normality tests, an alternate approach is to use graphical methods. The old adage, “A picture is worth a thousand words”. This captures the idea that the human mind is good at discerning patterns.
Statistical Process Control (SPC) charts allow timely detection of assignable causes of process changes (e.g. shifts, trends, variation) so that root causes may be determined and corrective actions taken before product performance is adversely impacted. Proper use of SPC identifies and eliminates “special cause” sources of variation. To achieve desired process capability, sources of “common cause” variation may need to be identified as well, using tools such as Design of Experiments to develop process understanding and predictive models that explain the source of the unwanted variability. [Read more…]
We are ready to learn how to analyze the data collected during the experiment. This is the most exciting part of DOE! We will cover the analysis in this article as well as the next several articles.
The following are the main steps to perform during the analysis:
“Team leadership is the secret that makes common people achieve uncommon results.” – Ifeanyi Onuoha
One of the key factors for successful application of FMEAs is skillful facilitation of FMEA teams. The skills needed for excellent facilitation are different from the skills needed to be a good FMEA team member. [Read more…]
“Everything’s computerized now. The backyard mechanic is a thing of the past!”, I recall my father declaring more than 20 years ago while leaning over the open hood of my mother’s Ford Windstar. He was referring to the relative simplicity of repairing the largely mechanical systems in automobiles from the 1950’s and 60’s versus those of the early 2000’s that were equipped with digital sensors and a central processor. Of course, today’s plug-in EV’s, advanced driver assist systems, 360-degree cameras, and “guardians” like General Motors’ OnStar make my mom’s old minivan look like an Anglia in comparison.