Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
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.
When applied properly, SPC provides manufacturers a proven method to increase profitability and achieve a deeper understanding of their processes.. Additionally, SPC can prevent problems—saving companies money that would have been lost in scrap, rework, warranty, litigation, and market share decline. A key factor in obtaining these SPC benefits is the proper deployment of control charts. Correctly designed control charts identify significant changes to a process. These can be changes that are still within specification, but are statistically different than where the process was previously running. By identifying the changes, personnel can determine what caused the change and potentially improve the process or prevent the production of inferior products. [Read more…]
The ASQ Reliability Division (RD), copyrighted the 2003 monograph “Credible Reliability Prediction” (CRP) but lost all copies circa 2014. I pestered the RD to let me republish CRP, because people asked “How do I make credible reliability predictions?” Copyright reversion to authors is accepted practice when a publisher no longer supports a document.
[Read more…]
In this week’s article entry, we discuss some guidelines for conducting an experiment. As we discussed in an earlier post, planning the study is critical for a successful outcome. A good plan makes the conduct of the study straightforward.
Below are some key aspects of conducting the study: [Read more…]
ASQ Reliability Division published “Credible Reliability Prediction” (CRP) in 2003. Harold Williams, Reliability Division monograph series editor, wrote, “[CRP] …delineates statistical methods that effectively extend MTBF prediction to complex, redundant, dependent, standby, and life-limited systems… This is the first text that describes a credible method of making age-specific reliability predictions…. This monograph presents insights and information inspired by real applications and [still] not covered in contemporary reliability textbooks.”
Manufacturing companies are struggling with the persistent and growing problem of finding employees with the skills needed to sustain and grow their businesses. This problem is commonly called the “skills gap.” Other sectors like construction are also affected by this skills gap. But at the macroeconomic level, the skills gap in manufacturing is particularly profound because of the growth in other sectors that manufacturing influences.
In this article post, we discuss several important techniques to consider when conducting and analyzing an experiment. They are summarized in the table below and next we discuss each one in a bit more detail. Some of these techniques are bit more advanced (e.g. Blocking, Covariates), but they are introduced here. [Read more…]
