Fleet Data Analysis - Revision history

David J. Groebel at 17:17, 20 June 2014

2014-06-20T17:17:29Z

← Older revision		Revision as of 17:17, 20 June 2014
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	Recall from the previous chapter that in order to make projections using the [[Crow Extended]] model, the <math>\beta \,\!</math> of the combined A and BD modes should be equal to 1. Since the failure intensity in a fielded system might be changing over time (e.g., increasing if the system wears out), this assumption might be violated. In such a scenario, the Crow Extended model cannot be used. However, if a fleet of systems is considered and the number of fleet failures versus fleet time is modeled, the failures might become random. This is because there is a mixture of systems within a fleet, new and old, and when the failures of this mixture of systems are viewed from a cumulative fleet time point of view, they may be random. The next two figures illustrate this concept. The first picture shows the number of failures over system age. It can be clearly seen that as the systems age, the intensity of the failures increases (wearout). The superposition system line, which brings the failures from the different systems under a single timeline, also illustrates this observation. On the other hand, if you take the same four systems and combine their failures from a fleet perspective, and consider fleet failures over cumulative fleet hours, then the failures seem to be random. The second picture illustrates this concept in the System Operation plot when you consider the Cum. Time Line. In this case, the <math>\beta \,\!</math> of the fleet will be equal to 1 and the Crow Extended model can be used for quantifying the effects of future reliability improvements on the fleet.		Recall from the previous chapter that in order to make projections using the [[Crow Extended]] model, the <math>\beta \,\!</math> of the combined A and BD modes should be equal to 1. Since the failure intensity in a fielded system might be changing over time (e.g., increasing if the system wears out), this assumption might be violated. In such a scenario, the Crow Extended model cannot be used. However, if a fleet of systems is considered and the number of fleet failures versus fleet time is modeled, the failures might become random. This is because there is a mixture of systems within a fleet, new and old, and when the failures of this mixture of systems are viewed from a cumulative fleet time point of view, they may be random. The next two figures illustrate this concept. The first picture shows the number of failures over system age. It can be clearly seen that as the systems age, the intensity of the failures increases (wearout). The superposition system line, which brings the failures from the different systems under a single timeline, also illustrates this observation. On the other hand, if you take the same four systems and combine their failures from a fleet perspective, and consider fleet failures over cumulative fleet hours, then the failures seem to be random. The second picture illustrates this concept in the System Operation plot when you consider the Cum. Time Line. In this case, the <math>\beta \,\!</math> of the fleet will be equal to 1 and the Crow Extended model can be used for quantifying the effects of future reliability improvements on the fleet.

	[[Image:rga13.5.png~~\|thumb~~\|center\|~~400px\|Repairable System Operation plot.~~]]		[[Image:rga13.5.png\|center\|450px]]

	[[Image:rga13.6.png~~\|thumb~~\|center\|~~400px\|Fleet System Operation plot.~~]]		[[Image:rga13.6.png\|center\|450px]]

	==Methodology==		==Methodology==

Chris Kahn: /* Applying the Crow Extended Model to Fleet Data */

2014-06-12T16:19:35Z

Applying the Crow Extended Model to Fleet Data

← Older revision		Revision as of 16:19, 12 June 2014
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	{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"		{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
	\|-		\|-
	\|colspan="4" style="text-align:center"\|'''Grouped Data for Distinct BD ~~modes~~ from<br>[[Fleet_Data_Analysis#Applying_the_Crow_Extended_Model_to_Fleet_Data\|Applying the Crow Extended Model to Fleet Data]]		\|colspan="4" style="text-align:center"\|'''Grouped Data for Distinct BD Modes from Data in <br> "[[Fleet_Data_Analysis#Applying_the_Crow_Extended_Model_to_Fleet_Data\|Applying the Crow Extended Model to Fleet Data]]"
	\|-		\|-
	!Interval		!Interval

Chris Kahn: /* Applying the Crow Extended Model to Fleet Data */

2014-06-12T16:17:18Z

Applying the Crow Extended Model to Fleet Data

← Older revision		Revision as of 16:17, 12 June 2014
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	{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"		{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
	\|-		\|-
	\|colspan="4" style="text-align:center"\|'''Grouped Data for Distinct BD modes from [[Fleet_Data_Analysis#Applying_the_Crow_Extended_Model_to_Fleet_Data\|Applying the Crow Extended Model to Fleet Data]]		\|colspan="4" style="text-align:center"\|'''Grouped Data for Distinct BD modes from<br>[[Fleet_Data_Analysis#Applying_the_Crow_Extended_Model_to_Fleet_Data\|Applying the Crow Extended Model to Fleet Data]]
	\|-		\|-
	!Interval		!Interval

Chris Kahn at 16:15, 12 June 2014

2014-06-12T16:15:53Z

← Older revision		Revision as of 16:15, 12 June 2014
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	{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"		{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
	\|-		\|-
	\|colspan="4" style="text-align:center"\|'''Grouped ~~data~~ for ~~distinct~~ BD modes from ~~Table 13.6'''~~		\|colspan="4" style="text-align:center"\|'''Grouped Data for Distinct BD modes from [[Fleet_Data_Analysis#Applying_the_Crow_Extended_Model_to_Fleet_Data\|Applying the Crow Extended Model to Fleet Data]]
	\|-		\|-
	!Interval		!Interval

Chris Kahn: /* Applying the Crow Extended Model to Fleet Data */

2014-06-12T15:41:33Z

Applying the Crow Extended Model to Fleet Data

← Older revision		Revision as of 15:41, 12 June 2014
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	{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"		{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
	\|-		\|-
	\|colspan="4" style="text-align:center"\|'''Grouped ~~data~~ for ~~distinct~~ BD ~~modes~~'''		\|colspan="4" style="text-align:center"\|'''Grouped Data for Distinct BD Modes'''
	\|-		\|-
	!Interval		!Interval

Chris Kahn: /* Applying the Crow Extended Model to Fleet Data */

2014-06-12T15:34:20Z

Applying the Crow Extended Model to Fleet Data

← Older revision		Revision as of 15:34, 12 June 2014
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	{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"		{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
	\|-		\|-
	\|colspan="7" style="text-align:center"\|'''Test-~~find~~-~~test fleet data~~'''		\|colspan="7" style="text-align:center"\|'''Test-Find-Test Fleet Data'''
	\|-		\|-
	!<math>q\,\!</math>		!<math>q\,\!</math>

Chris Kahn: /* Methodology */

2014-06-11T23:58:45Z

Methodology

← Older revision		Revision as of 23:58, 11 June 2014
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	{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"		{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
	\|-		\|-
	\|colspan="3" style="text-align:center"\|'''System ~~data~~'''		\|colspan="3" style="text-align:center"\|'''System Data'''
	\|-		\|-
	!System		!System
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	{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"		{\|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
	\|-		\|-
	\|colspan="2" style="text-align:center"\|'''Grouped ~~data~~'''		\|colspan="2" style="text-align:center"\|'''Grouped Data'''
	\|-		\|-
	!Failures in Interval		!Failures in Interval

David J. Groebel at 23:06, 22 April 2014

2014-04-22T23:06:41Z

Show changes

Kate Racaza: /* Predicting the Number of Failures for Fleet Operation */

2014-01-29T19:17:18Z

Predicting the Number of Failures for Fleet Operation

@@ Line 261: / Line 261: @@
 =More Examples=
 ==Predicting the Number of Failures for Fleet Operation==
-systems from the field were chosen for fleet analysis. Each system had at least one failure. All of the systems had a start time equal to zero and the last failure for each system corresponds to the end time. Group the data based on a fixed interval of 3,000 hours, and assume a fixed effectiveness factor equal to 0.4. Do the following:
+{{:Crow_Extended_Model_Fleet_Analysis_Example}}

Kate Racaza: /* Fleet Analysis Example */

2014-01-29T19:12:24Z

Fleet Analysis Example

@@ Line 83: / Line 83: @@
 ==Fleet Analysis Example==
-The following table presents data for a fleet of 27 systems. A cycle is a complete history from overhaul to overhaul. The failure history for the last completed cycle for each system is recorded. This is a random sample of data from the fleet. These systems are in the order in which they were selected. Suppose the intervals to group the current data are 10,000; 20,000; 30,000; 40,000 and the final interval is defined by the termination time. Conduct the fleet analysis.
+{{:Fleet_Analysis_Example}}
 ==Applying the Crow Extended Model to Fleet Data==