Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of articles by:
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…]
An earlier article focused on the conceptual application of appropriate sample sizes for X-bar charts. As we discussed, the purpose of control charts is to detect significant process changes when they occur. When the proper sample size is selected, X-bar charts will detect process shifts (that have practical significance) in a timely manner. [Read more…]
Imagine riding your motor scooter one sunny afternoon to your auntie’s home who lives near the sea, 20 miles from your home. On your trip there, the wind is at your back and the terrain slopes downward, resulting in an average speed of 25 mph. On the way home however, you’re motoring on a slight upward slope and into the wind, resulting in an average speed of 15 mph.
FMEA has an important relationship with risk management. This article provides an example of this relationship.
The suggested sequence is to first read the article “Relationship between FMEA and Risk Management” to learn about the interactions between FMEA and Risk Management at a high level. [Read more…]
In this article post, we discuss the suggested steps for planning a Designed Experiment.
It is critical to clearly define the problem before beginning experimentation. When the problem is not clearly defined and described, there will be confusion in designing and executing effective studies. To define appropriate responses to measure requires that the problem be understood and agreed on. Also, it is key to define the objectives of the actual experiment. If the problem is to reduce scrap rate, how much of a reduction is targeted? [Read more…]
“Component D” had some failures in its first 12 months. How many more would fail in 36-month warranty? ASQ’s Quality Progress Statistics Roundtable published the data and Weibull analysis. The data included left-censored failure counts collected at one calendar time. The Weibull analysis included actuarial failure forecasts. This article describes nonparametric alternatives to Weibull and quantifies extrapolation uncertainty. The nonparametric forecasts are larger than the Weibull forecasts. Alternative extrapolations of nonparametric failure rates from data subsets quantify uncertainty. [Read more…]
The purpose of control charts is to detect significant process changes when they occur. In general, charts that display averages of data/measurements (X-bar charts) are more useful than charts of individual data points or measurements. Charts of individuals are not nearly as sensitive as charts of averages at detecting process changes quickly. X-bar charts are far superior at detecting process shifts in a timely manner, and the sample size is a crucial element in ensuring that appropriate chart signals are produced. [Read more…]
While the construction of control charts is relatively straight-forward, often a more difficult question is “how do I know what process characteristic to control in the first place?” Clearly, controlling “everything” is not feasible or a smart use of limited resources. [Read more…]
I learned actuarial methods working for the USAF Logistics Command. We used actuarial rates to forecast demands and recommend stock levels for expensive engines tracked by serial number, hours, and cycles. I had a hunch that actuarial methods could be applied to all service parts, without life data. [Read more…]
In this article post, we formally define or describe the basic terminology that is commonly used in Design of Experiments. Some of the terms we have already been using in prior posts, but they will also be presented here for completeness. This is Part I of a two part article covering DOE Terminology. [Read more…]
Ergodicity means that cross-section probabilities equal longitudinal lifetime probabilities. (“Ergos” is Greek for “work.” Think of “ergonomics”.) Ergodicity means that we can estimate age-specific field reliability functions from cross-section data: ships (installed base) and returns (complaints, failures, service parts’ sales, etc.). Ships and returns provide information about lifetimes. Returns are the superpositions of failures of products or their parts started at different times. What does ergodicity have to do with toilet paper? [Read more…]
“If no one ever took risks, Michelangelo would have painted the Sistine floor.” – Neil Simon
FMEA and Risk Management are two distinct bodies of knowledge. This article explores some of the interactions between these two important methods. It is not intended to be a complete analysis of the similarities, differences, and linkages between FMEA and risk management. Rather, it is a high-level overview.
