Template:Difference detection matrix - Revision history

Kate Racaza: Redirected page to Reliability Test Design

2012-06-29T08:05:50Z

Redirected page to Reliability Test Design

← Older revision		Revision as of 08:05, 29 June 2012
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	~~==Test Design Using a Life Difference Detection Matrix==~~		#REDIRECT [[Reliability Test Design]]

	Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long should the test be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) techniques should be utilized. For a simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.

	As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This approach is also used by the difference detection matrix.

	~~Assume we want to compare the B10 lifes (or mean lifes) of two designs. The test is time-terminated and the termination time is set to ''T''. Using the method given in~~ [[Reliability Test Design~~#Test Design Using Expected Failure Time Plots\|Test Design Using Expected Failure Time Plots~~]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lifes cannot be detected from this test. We have to either increase the sample size or the test duration.


	~~'''Example 7:'''~~
	~~{{Example: Test Design Using Life Difference Detection Matrix}}~~

Kate Racaza at 15:20, 28 June 2012

2012-06-28T15:20:25Z

← Older revision		Revision as of 15:20, 28 June 2012
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	As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This approach is also used by the difference detection matrix.		As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This approach is also used by the difference detection matrix.

	Assume we want to compare the B10 lifes (or mean lifes) of two designs. The test is time-terminated and the termination time is set to ''T''. Using the method given in [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lifes cannot be detected from this test. We have to either increase the sample size or the test duration.		Assume we want to compare the B10 lifes (or mean lifes) of two designs. The test is time-terminated and the termination time is set to ''T''. Using the method given in [[Reliability Test Design#Test Design Using Expected Failure Time Plots\|Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lifes cannot be detected from this test. We have to either increase the sample size or the test duration.


	'''Example 7:'''		'''Example 7:'''
	{{Example: Test Design Using Life Difference Detection Matrix}}		{{Example: Test Design Using Life Difference Detection Matrix}}

Lisa Hacker: /* Test Design Using Life Difference Detection Matrix */

2012-03-30T00:25:01Z

Test Design Using Life Difference Detection Matrix

← Older revision		Revision as of 00:25, 30 March 2012
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	==Test Design Using Life Difference Detection Matrix==		==Test Design Using a Life Difference Detection Matrix==

	Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long the test ~~should~~ be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) techniques should be utilized. For simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.		Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long should the test be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) techniques should be utilized. For a simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.

	As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This is also used by the difference detection matrix.		As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This approach is also used by the difference detection matrix.

	Assume we want to compare the B10 ~~lives~~ (or mean ~~lives~~) of two designs. The test is time terminated and the termination time is set to ''T''. Using the method given in ~~section~~ [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 ~~lives~~ cannot be detected from this test. We have to either increase the sample size or the test duration.		Assume we want to compare the B10 lifes (or mean lifes) of two designs. The test is time-terminated and the termination time is set to ''T''. Using the method given in [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lifes cannot be detected from this test. We have to either increase the sample size or the test duration.


	'''Example 7:'''		'''Example 7:'''
	{{Example: Test Design Using Life Difference Detection Matrix}}		{{Example: Test Design Using Life Difference Detection Matrix}}

Chris Kahn: /* Test Design Using Life Difference Detection Matrix */

2012-03-08T18:49:54Z

Test Design Using Life Difference Detection Matrix

← Older revision		Revision as of 18:49, 8 March 2012
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	==Test Design Using Life Difference Detection Matrix==		==Test Design Using Life Difference Detection Matrix==

	Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long the test should be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) ~~techiques~~ should be utilized. For simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.		Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long the test should be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) techniques should be utilized. For simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.

	As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This is also used by the difference detection matrix.		As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This is also used by the difference detection matrix.

	Assume we want to compare the B10 lives (or mean lives) of two designs. The test is time terminated and the ~~terimation~~ time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or the test duration.		Assume we want to compare the B10 lives (or mean lives) of two designs. The test is time terminated and the termination time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or the test duration.


	'''Example 7:'''		'''Example 7:'''
	{{Example: Test Design Using Life Difference Detection Matrix}}		{{Example: Test Design Using Life Difference Detection Matrix}}

Harry Guo: /* Test Design Using Life Difference Detection Matrix */

2012-02-24T21:54:39Z

Test Design Using Life Difference Detection Matrix

← Older revision		Revision as of 21:54, 24 February 2012
Line 2:		Line 2:

	Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long the test should be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) techiques should be utilized. For simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.		Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long the test should be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) techiques should be utilized. For simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.


	As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This is also used by the difference detection matrix.		As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This is also used by the difference detection matrix.

			Assume we want to compare the B10 lives (or mean lives) of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or the test duration.

	Assume we want to compare the B10 lives of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or the test duration.

	'''Example 7:'''		'''Example 7:'''
	{{Example: Test Design Using Life Difference Detection Matrix}}		{{Example: Test Design Using Life Difference Detection Matrix}}

Harry Guo: /* Test Design Using Life Difference Detection Matrix */

2012-02-24T18:43:40Z

Test Design Using Life Difference Detection Matrix

← Older revision		Revision as of 18:43, 24 February 2012
Line 7:		Line 7:


	Assume we want to compare the B10 lives of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, it B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or ~~increase~~ the test duration.		Assume we want to compare the B10 lives of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, its B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or the test duration.

	'''Example 7:'''		'''Example 7:'''
	{{Example: Test Design Using Life Difference Detection Matrix}}		{{Example: Test Design Using Life Difference Detection Matrix}}

Harry Guo: /* Test Design Using Life Difference Detection Matrix */

2012-02-24T18:35:38Z

Test Design Using Life Difference Detection Matrix

@@ Line 9: / Line 9: @@
 Assume we want to compare the B10 lives of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, it B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or increase the test duration.
+'''Example 7:'''
+{{Example: Test Design Using Life Difference Detection Matrix}}

Harry Guo at 18:34, 24 February 2012

2012-02-24T18:34:25Z

← Older revision		Revision as of 18:34, 24 February 2012
Line 7:		Line 7:


	Assume we want to compare the B10 lives of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, it B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two confidence intervals ~~of the two designs~~ overlap with each other,		Assume we want to compare the B10 lives of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, it B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two estimated confidence intervals overlap with each other, it means the difference of the two B10 lives cannot be detected from this test. We have to either increase the sample size or increase the test duration.

Harry Guo at 18:28, 24 February 2012

2012-02-24T18:28:32Z

← Older revision		Revision as of 18:28, 24 February 2012
Line 1:		Line 1:
	==Test Design Using Life Difference Detection Matrix==		==Test Design Using Life Difference Detection Matrix==

	~~Often times, engineers~~ need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long the test should be conducted in order to detect a certain amount of difference. There is no simple ~~answer~~. Usually, advanced design of experiments (DOE) techiques should be utilized. For simple case, such as comparing two designs, the Difference Detection Matrix can be used. The difference detection matrix graphically indicates the amount of test time required to detect a ~~statistically~~ difference in the lives of two populations.		Engineers often need to design tests for detecting life differences between two or more product designs. The questions are how many samples and how long the test should be conducted in order to detect a certain amount of difference. There are no simple answers. Usually, advanced design of experiments (DOE) techiques should be utilized. For simple case, such as comparing two designs, the Difference Detection Matrix in Weibull++ can be used. The difference detection matrix graphically indicates the amount of test time required to detect a statistical difference in the lives of two populations.


			As discussed in the test design using expected failure times plot, if the sample size is known, the expected failure time of each test unit can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]] where random failure times are generated from simulation and then used to estimate the failure distribution. This is also used by the difference detection matrix.


			Assume we want to compare the B10 lives of two designs. The test is time terminated and the terimation time is set to ''T''. Using the method given in section [[Test Design Using Expected Failure Time Plots]], we can generate the failure times. For any failure time greater than ''T'', it is a suspension and the suspension time is ''T''. For each design, it B10 life and confidence bounds can be estimated from the generated failure/suspension times. If the two confidence intervals of the two designs overlap with each other,




	As discussed in the test design using expected failure tiimes plot, if the sample size is known, the expected failure time of each test sample can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]]. This is also the theory behind the difference detection matrix.


	~~Assume we want to compare the B10 lives of two designs. The test duration is set to ''T''. Using the method givne in [[test design using expected failure tiimes plot]].~~

	The Difference Detection Matrix takes as inputs information about the samples and information about the testing setup. For each sample, the Difference Detection Matrix needs the size of the sample, the distribution to be assumed for the sample population’s life, and a parameter affecting the variance of each population’s life distribution (e.g., beta for Weibull, and sigma for Normal and Lognormal). The Difference Detection Matrix also needs to know what the maximum allowable test time is, and by what amount of time to increment to the mean lives to be compared. For example, in the design shown below, the mean lives are incremented by 300, up to a mean life of 3000.		The Difference Detection Matrix takes as inputs information about the samples and information about the testing setup. For each sample, the Difference Detection Matrix needs the size of the sample, the distribution to be assumed for the sample population’s life, and a parameter affecting the variance of each population’s life distribution (e.g., beta for Weibull, and sigma for Normal and Lognormal). The Difference Detection Matrix also needs to know what the maximum allowable test time is, and by what amount of time to increment to the mean lives to be compared. For example, in the design shown below, the mean lives are incremented by 300, up to a mean life of 3000.

Harry Guo: /* Test Design Using Life Difference Detection Matrix */

2012-02-24T18:10:47Z

Test Design Using Life Difference Detection Matrix

← Older revision		Revision as of 18:10, 24 February 2012
Line 4:		Line 4:


	As discussed in the test design using expected failure tiimes plot, if the sample size is known, the expected failure time of each test sample can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[~~Test Design Using~~ SimuMatic\|Simumatic]]. This is also the theory behind the difference detection matrix.		As discussed in the test design using expected failure tiimes plot, if the sample size is known, the expected failure time of each test sample can be obtained based on the assumed failure distribution. Now let's go one step further. With these failure times, we can then estimate the failure distribution and calculate any reliability metrics. This process is similar to the simulation used in [[Weibull SimuMatic\|Simumatic]]. This is also the theory behind the difference detection matrix.