Reliability Growth Planning - Revision history

Kate Racaza: Updated for V11

2017-01-31T21:24:56Z

Updated for V11

Chris Kahn: /* Effectiveness Factor */

2014-05-15T21:59:39Z

Effectiveness Factor

← Older revision		Revision as of 21:59, 15 May 2014
Line 54:		Line 54:

	===Effectiveness Factor===		===Effectiveness Factor===
	When a delayed corrective action is implemented for a type B failure mode, in other words a BD mode, the failure intensity for that mode is reduced if the corrective action is effective. Once a BD mode is discovered, it is rarely totally eliminated by a corrective action. After a BD mode has been found and fixed, a certain percentage of the failure intensity will be removed, but a certain percentage of the failure intensity will generally remain. The fraction decrease in the BD mode failure intensity due to corrective actions, <math>d\,\!</math>, <math>\left( 0<d<1 \right),\,\!</math> is called the ''effectiveness factor'' (EF). A study on EFs showed that an average EF, <math>d,\,\!</math> is about 70%. Therefore, about 30%, (i.e., 100(1-''d'')%), of the BD mode failure intensity will typically remain in the system after all of the corrective actions have been implemented. However, individual EFs for the failure modes may be larger or smaller than the average. This average value of 70% can be used for planning purposes, or if such information is recorded, an average effectiveness factor from a previous reliability growth program can be used.		When a delayed corrective action is implemented for a type B failure mode, in other words a BD mode, the failure intensity for that mode is reduced if the corrective action is effective. Once a BD mode is discovered, it is rarely totally eliminated by a corrective action. After a BD mode has been found and fixed, a certain percentage of the failure intensity will be removed, but a certain percentage of the failure intensity will generally remain. The fraction decrease in the BD mode failure intensity due to corrective actions, <math>d\,\!</math>, <math>\left( 0<d<1 \right),\,\!</math> is called the ''effectiveness factor'' (EF).

			A study on EFs showed that an average EF, <math>d\,\!</math>, is about 70%. Therefore, about 30%, (i.e., <math>100(1-d)%\,\!</math>), of the BD mode failure intensity will typically remain in the system after all of the corrective actions have been implemented. However, individual EFs for the failure modes may be larger or smaller than the average. This average value of 70% can be used for planning purposes, or if such information is recorded, an average effectiveness factor from a previous reliability growth program can be used.

	===MTBF Goal===		===MTBF Goal===

Chris Kahn: /* Effectiveness Factor */

2014-05-15T21:58:39Z

Effectiveness Factor

← Older revision		Revision as of 21:58, 15 May 2014
Line 54:		Line 54:

	===Effectiveness Factor===		===Effectiveness Factor===
	When a delayed corrective action is implemented for a type B failure mode, in other words a BD mode, the failure intensity for that mode is reduced if the corrective action is effective. Once a BD mode is discovered, it is rarely totally eliminated by a corrective action. After a BD mode has been found and fixed, a certain percentage of the failure intensity will be removed, but a certain percentage of the failure intensity will generally remain. The fraction decrease in the BD mode failure intensity due to corrective actions, <math>d\,\!</math>, <math>\left( 0<d<1 \right),\,\!</math> is called the ''effectiveness factor'' (EF). A study on EFs showed that an average EF, <math>d,\,\!</math> is about 70%. Therefore, about 30%, (i.e., ~~<math>~~100(1-d)%~~\,\!</math>~~) , of the BD mode failure intensity will typically remain in the system after all of the corrective actions have been implemented. However, individual EFs for the failure modes may be larger or smaller than the average. This average value of 70% can be used for planning purposes, or if such information is recorded, an average effectiveness factor from a previous reliability growth program can be used.		When a delayed corrective action is implemented for a type B failure mode, in other words a BD mode, the failure intensity for that mode is reduced if the corrective action is effective. Once a BD mode is discovered, it is rarely totally eliminated by a corrective action. After a BD mode has been found and fixed, a certain percentage of the failure intensity will be removed, but a certain percentage of the failure intensity will generally remain. The fraction decrease in the BD mode failure intensity due to corrective actions, <math>d\,\!</math>, <math>\left( 0<d<1 \right),\,\!</math> is called the ''effectiveness factor'' (EF). A study on EFs showed that an average EF, <math>d,\,\!</math> is about 70%. Therefore, about 30%, (i.e., 100(1-''d'')%), of the BD mode failure intensity will typically remain in the system after all of the corrective actions have been implemented. However, individual EFs for the failure modes may be larger or smaller than the average. This average value of 70% can be used for planning purposes, or if such information is recorded, an average effectiveness factor from a previous reliability growth program can be used.

	===MTBF Goal===		===MTBF Goal===

Chris Kahn: /* Fix Delays and Test Phase Target MTBF */

2014-05-15T20:40:02Z

Fix Delays and Test Phase Target MTBF

← Older revision		Revision as of 20:40, 15 May 2014
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	In other words, the average fix delay is calendar time converted to test hours. For example, say that we expect an average fix delay of two weeks. If in two weeks the total test time is 1,000 hours, then the average fix delay is 1,000 hours. If in the same two weeks the total test time is 2,000 hours (maybe there are more units available or more shifts), then the average fix delay is 2,000 hours.		In other words, the average fix delay is calendar time converted to test hours. For example, say that we expect an average fix delay of two weeks. If in two weeks the total test time is 1,000 hours, then the average fix delay is 1,000 hours. If in the same two weeks the total test time is 2,000 hours (maybe there are more units available or more shifts), then the average fix delay is 2,000 hours.

	There can be a constant fix delay across all test phases or, as a practical matter, each test phase can have a different fix delay time. In practice, the fix delay will generally be constant over a particular test phase. <math>{{L}_{i}}\,\!</math> denotes the fix delay for phase <math>i=1,...,P,\,\!</math> where <math>P\,\!</math> is the total number of phases in the test. The RGA software allows for a maximum of ~~seven~~ test phases.		There can be a constant fix delay across all test phases or, as a practical matter, each test phase can have a different fix delay time. In practice, the fix delay will generally be constant over a particular test phase. <math>{{L}_{i}}\,\!</math> denotes the fix delay for phase <math>i=1,...,P,\,\!</math> where <math>P\,\!</math> is the total number of phases in the test. The RGA software allows for a maximum of ten test phases.

	===Actual Failure Intensity Function===		===Actual Failure Intensity Function===

Chris Kahn: /* Effectiveness Factor */

2014-05-15T20:38:35Z

Effectiveness Factor

← Older revision		Revision as of 20:38, 15 May 2014
Line 54:		Line 54:

	===Effectiveness Factor===		===Effectiveness Factor===
	When a delayed corrective action is implemented for a type B failure mode, in other words a BD mode, the failure intensity for that mode is reduced if the corrective action is effective. Once a BD mode is discovered, it is rarely totally eliminated by a corrective action. After a BD mode has been found and fixed, a certain percentage of the failure intensity will be removed, but a certain percentage of the failure intensity will generally remain. The fraction decrease in the BD mode failure intensity due to corrective actions, <math>d\,\!</math>, <math>\left( 0<d<1 \right),\,\!</math> is called the ''effectiveness factor'' (EF). A study on EFs showed that an average EF, <math>d,\,\!</math> is about 70%. Therefore, about 30%, (i.e., <math>100(1-d)%\,\!</math>), of the BD mode failure intensity will typically remain in the system after all of the corrective actions have been implemented. However, individual EFs for the failure modes may be larger or smaller than the average. This average value of 70% can be used for planning purposes, or if such information is recorded, an average effectiveness factor from a previous reliability growth program can be used.		When a delayed corrective action is implemented for a type B failure mode, in other words a BD mode, the failure intensity for that mode is reduced if the corrective action is effective. Once a BD mode is discovered, it is rarely totally eliminated by a corrective action. After a BD mode has been found and fixed, a certain percentage of the failure intensity will be removed, but a certain percentage of the failure intensity will generally remain. The fraction decrease in the BD mode failure intensity due to corrective actions, <math>d\,\!</math>, <math>\left( 0<d<1 \right),\,\!</math> is called the ''effectiveness factor'' (EF). A study on EFs showed that an average EF, <math>d,\,\!</math> is about 70%. Therefore, about 30%, (i.e., <math>100(1-d)%\,\!</math>) , of the BD mode failure intensity will typically remain in the system after all of the corrective actions have been implemented. However, individual EFs for the failure modes may be larger or smaller than the average. This average value of 70% can be used for planning purposes, or if such information is recorded, an average effectiveness factor from a previous reliability growth program can be used.

	===MTBF Goal===		===MTBF Goal===

David J. Groebel at 21:23, 22 April 2014

2014-04-22T21:23:05Z

Show changes

Kate Racaza: /* Growth Plan for 4 Phases */

2014-01-29T18:33:56Z

Growth Plan for 4 Phases

← Older revision		Revision as of 18:33, 29 January 2014
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	===Growth Plan for 4 Phases===		===Growth Plan for 4 Phases===
	The reliability team of a product manufacturer has put together a reliability growth plan, based on the Crow extended model, for one of their new products. The reliability growth model was constructed with the following inputs:		{{:Growth_Plan_for_Four_Phases}}

	~~:*The requirement or goal MTBF is <math>~~{{~~M}_{G}}=910\,\!</math> hours.~~
	~~:*The growth potential design margin factor is <math>GPDM=1.35\,\!</math>.~~
	~~:*The average effectiveness factor is <math>d\quad =0.7.\,\!</math>~~
	~~:*The management strategy is <math>msr=0.95\,\!</math>.~~
	~~:*The beta parameter for the discovery function, <math>h\left( t \right),\,\!</math> of the type B failure modes is <math>\beta =0.70\,\!</math>.~~
	:*The test is planned to be conducted in four phases. The cumulative phase end times are <math>{{T}_{1}}=19000,{{T}_{2}}=38000\,\!</math>, <math>{{T}_{3}}=70000\,\!</math> and <math>{{T}_{4}}=100000\,\!</math>. The average fix delay in terms of test hours for each phase is: ~~<math>{{L~~}~~_{1~~}~~}=10000\,\!</math>, <math>{{L}_{2}}=15000,\,\!</math> <math>{{L}_{3}}=20000\,\!</math> and <math>{{L}_{4}}=25000\,\!</math> hours.~~


	~~:1) Plot the nominal and actual reliability growth curves for this program, using the RGA software.~~

	:2) The reliability program was initiated and actual test data from phase 1 is now available. The test data were analyzed using the Crow extended-continuous evaluation model and the following figure shows the results. Note that the ''I'' events, which represent the times for implementation of fixes for BD modes, occur at least 10,000 hours after the first occurrence of the specific BD mode. This is a reflection of the average fix delay being equal to 10,000 hours for the first test phase. Of course we could have some exceptions that could have been fixed before 10,000 hours after the first occurrence (i.e., discovery) of the mode, but on average, they should be fixed after 10,000 hours.

	~~[[Image:rga11.11.png\|thumb\|center\|450px\|Phase 1 data, analyzed with the Crow Extended- Continuous Evaluation model.]]~~

	The same applies to the delayed fixes at the end of phase 1. On average there should be at least 10,000 test hours between the discovery of the failure mode and the implementation of a fix for that mode, so failure modes discovered after 9,000 hours of testing in the first phase cannot be implemented at the end of the first phase.

	The figure below shows the effectiveness factor window. The record shows that the fixes for failure modes BD 11000, BD 12000 and BD 13000 are not implemented at the end of the first phase due to the fix delay. The effectiveness factor used is in accordance with the planning model and has been fixed to 0.7.

	~~[[Image:rga11.12.png\|thumb\|center\|450px\|Fixed effectiveness factor and phase of implementation for unfixed BD modes in Phase 1.]]~~

	Construct a multi-phase graph that shows the analysis points for the test data in phase 1, as compared to the nominal and actual idealized growth curves for this reliability growth plan. Is the program on track, so far, as compared to the plan?


	~~'''Solution'''~~

	~~1) The following figures show the plan inputs, phase durations and average fix delays as entered in the RGA software, together with the results of the growth model.~~

	~~[[Image:rga11.13.png\|thumb\|center\|450px\|Inputs and results in reliability growth planning model.]]~~

	~~:The next figure shows the nominal and actual idealized growth curves for this program, together with the program MTBF goal.~~

	~~[[Image:rga11.15.png\|thumb\|center\|450px\|Nominal and actual idealized growth curves with planned growth in four phases.]]~~


	2) The next step is to associate the reliability growth plan with the data from phase 1. To do that, right-click the '''Additional Plots''' header in the Current Project Explorer and choose '''Add MultiPhase Plot''' from the shortcut menu, then follow the wizard to specify the data sheet that contains the phase 1 data with the folio that defines the growth plan. The figure below shows the generated multiphase plot, which brings together the reliability growth planning model with the actual test results. This plot shows the demonstrated MTBF for each analysis point during the first phase of testing. Analysis points for projected and growth potential MTBF can also be plotted. For this example, it can be said that the program is on track since the demonstrated MTBF of the first phase is higher than the planned MTBF for that phase.

	~~[[Image:rga11.16.png\|thumb\|center\|450px\|Multi-Phase plot containing the overall growth plan and the actual test results for Phase 1.]]~~

	:The multiphase plot can be updated continuously as the growth program progresses and more test data become available. In that manner, the reliability team can assess the actual reliability performance against the established goals.

Kate Racaza: /* Growth Plan for 7 Phases */

2014-01-29T18:32:50Z

Growth Plan for 7 Phases

@@ Line 376: / Line 376: @@
 ===Growth Plan for 7 Phases===
-The reliability team of a product manufacturer is putting together a reliability growth plan for one of the new products under design. The team wants to create an overall reliability growth plan based on the Crow extended model. The inputs to the model are the following:
+{{:Growth_Plan_for_Seven_Phases}}
 ===Growth Plan for 4 Phases===

Kate Racaza: /* Growth Plan for 3 Phases */

2014-01-29T18:26:29Z

Growth Plan for 3 Phases

Show changes

Chris Kahn at 21:07, 22 January 2014

2014-01-22T21:07:57Z

← Older revision		Revision as of 21:07, 22 January 2014
Line 6:		Line 6:

	==Background==		==Background==
	The [[Duane Model\|Duane postulate]] is based on empirical observations, and it reflects a learning curve pattern for reliability growth. This identical learning curve pattern forms the basis of the [[Crow-AMSAA - NHPP\|Crow-AMSAA (NHPP) model]]. The Duane postulate is also reflected in the [[Crow Extended\|Crow extended]] model in the form of the discovery function <math>h(t)\,\!</math>.		The [[Duane Model\|Duane postulate]] is based on empirical observations, and it reflects a learning curve pattern for reliability growth. This identical learning curve pattern forms the basis of the [[Crow-AMSAA (NHPP)\|Crow-AMSAA (NHPP) model]]. The Duane postulate is also reflected in the [[Crow Extended\|Crow extended]] model in the form of the discovery function <math>h(t)\,\!</math>.

	The discovery function is the rate in which new, distinct problems are being discovered during reliability growth development testing. The Crow-AMSAA (NHPP) model is a special case of the discovery function. Consider that when a new and distinct failure mode is first seen, the testing is stopped and a corrective action is incorporated before the testing is resumed. In addition, suppose that the corrective action is highly effective that the failure mode is unlikely to be seen again. In this case, the only failures observed during the reliability growth test are the first occurrences of the failure modes. Therefore, if the Crow-AMSAA (NHPP) model and the Duane postulate are accepted as the pattern for a test-fix-test reliability growth testing program, then the form of the Crow-AMSAA (NHPP) model must be the form for the discovery function, <math>h(t)\,\!</math>.		The discovery function is the rate in which new, distinct problems are being discovered during reliability growth development testing. The Crow-AMSAA (NHPP) model is a special case of the discovery function. Consider that when a new and distinct failure mode is first seen, the testing is stopped and a corrective action is incorporated before the testing is resumed. In addition, suppose that the corrective action is highly effective that the failure mode is unlikely to be seen again. In this case, the only failures observed during the reliability growth test are the first occurrences of the failure modes. Therefore, if the Crow-AMSAA (NHPP) model and the Duane postulate are accepted as the pattern for a test-fix-test reliability growth testing program, then the form of the Crow-AMSAA (NHPP) model must be the form for the discovery function, <math>h(t)\,\!</math>.