Could the two designs' averages be the same, but their reliability be quite different? How can you be more scientific about comparing the reliability of the two proposed designs? But as a simple measure of central tendency, the sample average gives no information about the spread or shape of the distribution of failure times. A comparison of sample averages using a Student's t test reveals no statistical difference between the average cycles for Design A and the average cycles for Design B (p-value = 0.965).
Both designs had at least one failure before 400,000 cycles, yet clearly the average number of cycles before failure exceeds 400,000 for both designs. The data in Figure 1 don't clearly indicate whether either design meets the desired reliability goal. These 20 units were tested until their spring housings failed.1 Figure 1 shows the number of cycles before failure for each item tested. Ten units were assembled with each of the two housing designs (Design A and Design B). This reliability goal is expressed mathematically as R(400,000) 0.90. In other words, the toy company would like 90 percent of the spring housings to survive at least 400,000 cycles. The desired reliability at 400,000 cycles is 0.90. Imagine that you work for a toy company that wants to compare the reliability of two proposed designs for a jack-in-the-box spring housing. Let's ignore the formulas for now and start by looking at an example of Weibull analysis in action. In academia, Weibull analysis has modeled such diverse phenomena as the length of labor strikes, AIDS mortality and earthquake probabilities. Weibull analysis can make predictions about a product's life, compare the reliability of competing product designs, statistically establish warranty policies or proactively manage spare parts inventories, to name just a few common industrial applications. I predict readers in both groups will be glad they stuck around.įor the uninitiated, Weibull analysis is a method for modeling data sets containing values greater than zero, such as failure data. You haven't turned the page yet? Those of you who remain probably fall under one of two categories: those familiar with reliability data analysis, and Excel enthusiasts who are curious to learn one more way to exploit this versatile software. This article presents a how-to approach for one such advanced technique-Weibull analysis. And with a little guidance, users can employ more advanced statistical methods with Excel. But with some creativity, users can produce tools like control charts, Pareto charts and box-and-whisker plots (see "Using Excel for Data Analysis," Quality Digest, October 1997). Fewer still put these capabilities to work for process improvement, product improvement and profit.Most Excel users are aware of the common formulas and charts. Yet few people realize the extent of Excel's analytical capabilities. M any people use Microsoft Excel on a daily basis.
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