R-DOE Design: Difference between revisions

Latest revision as of 21:09, 18 September 2023

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R-DOE Design

This example validates the calculation of the reliability DOE design in Weibull++.

Reference Case

The data are from an example on pages 530 and 544 in the book Experiments, Planning, Analysis, and Parameter Design Optimization by C.F. Jeff Wu, Michael Hamad, John Wiley & Sons, Inc. 2000.

Data

Run	Factor					Failure Time
Run	A	B	C	D	E	Failure Time
1	-	-	-	-	-	(14, 16)	(20, [math]\displaystyle{ \infty\,\! }[/math])
2	-	-	+	+	+	(18,20)	(20, [math]\displaystyle{ \infty\,\! }[/math])
3	-	+	-	-	+	(8, 10)	(10, 12)
4	-	+	+	+	-	(18, 20)	(20, [math]\displaystyle{ \infty\,\! }[/math])
5	+	-	-	+	-	(20, [math]\displaystyle{ \infty\,\! }[/math])	(20, [math]\displaystyle{ \infty\,\! }[/math])
6	+	-	+	-	+	(12, 14)	(20, [math]\displaystyle{ \infty\,\! }[/math])
7	+	+	-	+	+	(16, 18)	(20, [math]\displaystyle{ \infty\,\! }[/math])
8	+	+	+	-	-	(12, 14)	(14, 16)

Result

From the book, the MLE and P values for the lognormal regression model are:

Effect	MLE	P value
Intercept	2.939	0.00
A	0.117	0.06
B	-0.201	0.00
AB	-0.049	0.43
C	-0.051	0.41
D	0.273	0.00
E	-0.153	0.02

Results in DOE++

The software results match the book results. The MLE information table is:

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-This example validates the calculation of the reliability DOE design in DOE++.
+This example validates the calculation of the reliability DOE design in Weibull++.
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R-DOE Design: Difference between revisions

Latest revision as of 21:09, 18 September 2023

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