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===Example===
==Example==
Consider the test-fix-find-test data of Example 2 in Chapter 9, which is shown again in Table 12.1. The total test time for this test is 400 hours. Note that for this example we assume one stopping point at the end of the test for the incorporation of the delayed fixes. Suppose that the data set represents a military system with Task 1 firing a gun, Task 2 moving under environment E1 and Task 3 moving under environment E2. For every hour of operation the operational profile states that the system operates in environment E1 70% of the time and in environment E2 30% of the time. In addition, for each hour of operation the gun must be fired 10 times.
Consider the test-fix-find-test data of Example 2 in Chapter 9, which is shown again in Table 12.1. The total test time for this test is 400 hours. Note that for this example we assume one stopping point at the end of the test for the incorporation of the delayed fixes. Suppose that the data set represents a military system with Task 1 firing a gun, Task 2 moving under environment E1 and Task 3 moving under environment E2. For every hour of operation the operational profile states that the system operates in environment E1 70% of the time and in environment E2 30% of the time. In addition, for each hour of operation the gun must be fired 10 times.
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Revision as of 00:46, 23 August 2012

Testing Methodology

The methodology described here will use the Crow Extended model (presented in Chapter 9) for data analysis. In order to have valid Crow Extended model assessments, it is required that the operational mission profile be conducted in a structured manner. Therefore, this testing methodology involves convergence and stopping points during the testing. A stopping point is when the testing is stopped for the expressed purpose of incorporating the Type BD delayed corrective actions. There may be more than one stopping point during a particular testing phase. For simplicity, the methodology with only one stopping point will be described; however, the methodology can be extended to the case of more than one stopping point. A convergence point is a time during the test when all the operational mission profile tasks meet their expected averages or fall within an acceptable range. At least three convergence points are required for a well-balanced test. The end of the test, time [math]\displaystyle{ T }[/math] , must be a convergence point. The test times between the convergence points do not have to be the same.

The objective of having the convergence points is to be able to apply the Crow Extended model directly in such a way that the projection and other key reliability growth parameters can be estimated in a valid fashion. To do this, the grouped data methodology is applied. Note that the methodology also can be used with the Crow-AMSAA (NHPP) model for a simpler analysis without the ability to estimate projected and growth potential reliability. For the Crow-AMSAA (NHPP) grouped data methodology, refer to Chapter 5, Section 3. For grouped data using the Crow Extended model, refer to Chapter 9, Section 6.

Example

Consider the test-fix-find-test data of Example 2 in Chapter 9, which is shown again in Table 12.1. The total test time for this test is 400 hours. Note that for this example we assume one stopping point at the end of the test for the incorporation of the delayed fixes. Suppose that the data set represents a military system with Task 1 firing a gun, Task 2 moving under environment E1 and Task 3 moving under environment E2. For every hour of operation the operational profile states that the system operates in environment E1 70% of the time and in environment E2 30% of the time. In addition, for each hour of operation the gun must be fired 10 times.

In general, it is difficult to manage an operational test so that these operational profiles are continuously met throughout the test. However, the operational mission profile methodology requires that these conditions be met on average at the convergence points. In practice, this almost always can be done with proper program and test management. The convergence points are set for the testing, often at interim assessment points. The process for controlling the convergence at these points involves monitoring a graph for each of the tasks. Table 12.2 shows the expected and actual results for each of the operational mission profiles.

Table 12.1 - Test-fix-find-test data
[math]\displaystyle{ i }[/math] [math]\displaystyle{ {{X}_{i}} }[/math] Mode [math]\displaystyle{ i }[/math] [math]\displaystyle{ {{X}_{i}} }[/math] Mode
1 0.7 BC17 29 192.7 BD11
2 3.7 BC17 30 213 A
3 13.2 BC17 31 244.8 A
4 15 BD1 32 249 BD12
5 17.6 BC18 33 250.8 A
6 25.3 BD2 34 260.1 BD1
7 47.5 BD3 35 263.5 BD8
8 54 BD4 36 273.1 A
9 54.5 BC19 37 274.7 BD6
10 56.4 BD5 38 282.8 BC27
11 63.6 A 39 285 BD13
12 72.2 BD5 40 304 BD9
13 99.2 BC20 41 315.4 BD4
14 99.6 BD6 42 317.1 A
15 100.3 BD7 43 320.6 A
16 102.5 A 44 324.5 BD12
17 112 BD8 45 324.9 BD10
18 112.2 BC21 46 342 BD5
19 120.9 BD2 47 350.2 BD3
20 121.9 BC22 48 355.2 BC28
21 125.5 BD9 49 364.6 BD10
22 133.4 BD10 50 364.9 A
23 151 BC23 51 366.3 BD2
24 163 BC24 52 373 BD8
25 164.7 BD9 53 379.4 BD14
26 174.5 BC25 54 389 BD15
27 177.4 BD10 55 394.9 A
28 191.6 BC26 56 395.2 BD16


Table 12.2 - Expected and actual results for profiles 1, 2, 3
Profile 1(gun firings) Profile 2(E1) Profile 3(E2)
Time Expected Actual Expected Actual Expected Actual
5 50 0 3.5 5 1.5 0
10 100 0 7 10 3 0
15 150 0 10.5 15 4.5 0
20 200 0 14 20 6 0
25 250 100 17.5 25 7.5 0
30 300 150 21 30 9 0
35 350 400 24.5 30 10.5 5
40 400 600 28 30 12 10
45 450 600 31.5 30 13.5 15
50 500 600 35 30 15 20
55 550 800 38.5 35 16.5 20
60 600 800 42 40 18 20
65 650 800 45.5 45 19.5 20
70 700 800 49 50 21 20
75 750 800 52.5 55 22.5 20
80 800 900 56 55 24 25
85 850 950 59.5 55 25.5 30
90 900 1000 63 60 27 30
95 950 1000 66.5 65 28.5 30
100 1000 1000 70 70 30 30
105 1050 1000 73.5 70 31.5 35
... ... ... ... ... ...
... ... ... ... ... ...
355 3550 3440 248.5 259 106.5 96
360 3600 3690 252 264 108 96
365 3650 3690 255.5 269 109.5 96
370 3700 3850 259 274 111 96
375 3750 3850 262.5 279 112.5 96
380 3800 3850 266 280 114 100
385 3850 3850 269.5 280 115.5 105
390 3900 3850 273 280 117 110
395 3950 4000 276.5 280 118.5 115
400 4000 4000 280 280 120 120


Figure Profile shows a portion of the expected and actual results for mission profile 1, as entered in RGA 7. A graph exists for each of the three tasks in this example. Each graph has a line with the expected average as a function of hours, and the corresponding actual value. When the actual value for a task meets the expected value then is a convergence for that task. A convergence point occurs when all of the tasks converge at the same time. At least three convergence points are required, one of which is the stopping point [math]\displaystyle{ T }[/math] . In our example, the total test time is 400 hours. The convergence points were chosen to be at 100, 250, 320 and 400 hours. Figure convergence points shows the data sheet that contains the convergence points in RGA 7.

[math]\displaystyle{ }[/math]

Entering expected and actual results for profile 1 in RGA 7.

The testing profiles are managed so that at these times the actual operational test profile equals the expected values for the three tasks or falls within an acceptable range. Figure firings shows the expected and actual gun firings. Figure E1 shows the expected and actual time in environment E1 and Figure E2 shows the expected and actual time in environment E2.

[math]\displaystyle{ }[/math]

Specifying convergence points in RGA 7.



Operational mission profile for gun firings.



Operational mission profile for time in environment E1.



Operational mission profile for time in environment E2.



Combined mission profile graph with convergence points.



The objective of having the convergence points is to be able to apply the Crow Extended model directly in such a way that the projection and other key reliability growth parameters can be estimated in a valid fashion. To do this, grouped data is applied using the Crow Extended model. For reliability growth assessments using grouped data, only information between time points in the testing is used. In our application, these time points are the convergence points: 100, 250, 320, and 400. Figure Combinedission shows all three mission profiles plotted in the same graph, together with the convergence points.


Table 12.3 gives the grouped data input corresponding to the data in Table 12.1. The parameters of the Crow Extended model for grouped data are then estimated, as explained in Chapter 9, Section 6. Table 12.4 shows the effectiveness factors (EFs) for each of the BD modes.


Table 12.3 - Grouped data at convergence points 100, 250, 320 and 400 hours
Number at Event Time to Event Classification Mode Number at Event Time to Event Classification Mode
3 100 BC 17 1 250 BC 26
1 100 BD 1 1 250 BD 11
1 100 BC 18 1 250 BD 12
1 100 BD 2 3 320 A
1 100 BD 3 1 320 BD 1
1 100 BD 4 1 320 BD 8
1 100 BC 19 1 320 BD 6
2 100 BD 5 1 320 BC 27
1 100 A 1 320 BD 13
1 100 BC 20 1 320 BD 9
1 100 BD 6 1 320 BD 4
1 250 BD 7 3 400 A
3 250 A 1 400 BD 12
1 250 BD 8 2 400 BD 10
1 250 BC 21 1 400 BD 5
1 250 BD 2 1 400 BD 3
1 250 BC 22 1 400 BC 28
2 250 BD 9 1 400 BD 2
2 250 BD 10 1 400 BD 8
1 250 BC 23 1 400 BD 14
1 250 BC 24 1 400 BD 15
1 250 BC 25 1 400 BD 16




Table 12.4 - Effectiveness Factors for delayed fixes
Mode Effectiveness Factor
1 0.67
2 0.72
3 0.77
4 0.77
5 0.87
6 0.92
7 0.50
8 0.85
9 0.89
10 0.74
11 0.70
12 0.63
13 0.64
14 0.72
15 0.69
16 0.46

Using the Crow Extended failure times Data Sheet shown in Figure Failure times data, we can analyze this data set based on a mission profile by clicking the mission profile icon:

[math]\displaystyle{ }[/math]

Failure times data.

[math]\displaystyle{ }[/math]

Selecting a mission profile.


A specific mission profile can then be associated with the Data Sheet, as shown in Figure selecting. This will group the failure times data into groups based on the convergence points that have already been specified when constructing the mission profile.

[math]\displaystyle{ }[/math]

Grouped data set prepared based on the mission profile convergence points.



Instantaneous, demonstrated, projected and growth potential MTBF for grouped data.



A new data sheet with the grouped data is created, as shown in Figure groupedata. The calculated results based on the grouped data are as follows:


Figure growth potential shows the instantaneous, demonstrated, projected and growth potential MTBF for the grouped data, based the mission profile grouping with intervals at the specified convergence points of the mission profile.