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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
|
| A
|
B
|
C
|
D
|
E
|
| 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:
