EMM 4103 Project
JT9D COMPONENT FAILURE ANALYSIS
The JT9D engine inventory reviewed in the presented study was installed on three different airframes, which included DC-10, B-747 and B-787. The review of the JT9D engine and these three airframes involved fourteen carriers, operating on a monthly average of 883 JT9D engines during the period of observation. The actuarial trending of in-flight shutdowns and unscheduled engine removals resulted in seven air carriers being examined more closely for specific component failure incidents.
The JT9D inventory was introduced into operational service in the mid-1960’s and had a rated thrust of 45.600 pounds. Various thrust growth models of the -7 engine were developed to include the -7Fat 48.000 pounds of thrust, the -7Q at 53.000 pounds of thrust and the -7R4G2 with 54.750 pounds of thrust. The JT9D-7R4G engine is considered to be a significant growth model of the engine, not just in thrust produced but in reliability enhancements. The table with failures related to the general components of the JT9D engine are presented below:
JT9D ENGINE INVENTORY FAILURE INCIDENT ANALYSIS
FOR SELECTED CARRIERS
COMPONENTS JT9D TOTAL* JT9D-7R4G
BEARINGS 35 2
AIRFOILS 80 1
CASES 13 0
FUEL / OIL SYSTEMS 84 5
CONTROLS & ACCESSORIES 366 7
OTHER 254 3
TOTAL 832 18
• Total lncludesJT9D-7R4G information
Review of the engine bearing failure incidents showed the #3 bearing to exhibit the highest number of failures with fifteen incidents reported. The failure modes documented involved leaking breather seals, carbon plugged scavenge tubes and cracked bearing compartments. For the six reported #4 bearing failure incidents, failed oil pressure lines, packings and the housing were the most common reported incidents.
Of the airfoil failure incidents reported, the eighth stage compressor blades and first stage turbine blades were the most prevalent. Ten eighth stage compressor blade failures were reported, with some failures in the root area of the blades. The thirty-seven first stage turbine blades reported generally failed one inch above the platform, although some failed in the root area. The turbine blade failures were due to material stress not foreign object damage (FOO).
The review of the failure data of the JT9D engine inventory showed controls and accessories to be a dominant failure trend. Of the 832 reported incidents for the five-year component trending period, 366 of all reported failures Involved controls and accessories. The following Table exhibits the type of component failures reported. Of the controls and accessories reviewed, the following five accessories exhibited the strongest trends: fuel control, fuel pump, engine vane controller (EVC), TT2 sensor and pressure ratio bleed control (PRBC). The fuel control and fuel pump each had numerous individual failure incidents as well as 25 reported incidents of removal of both items. Dual removals indicate the diagnostic trouble shooting information and practices were not sufficiently efficient to isolate fuel management problems to one particular item. In addition, dual removals of fuel controls and fuel pumps generate at least one good Item to depot repair, where a RETEST OK event occurs. This necessarily increases the workload and costs to the accessory backshop repair area.
Table of JT9D Engine Component Failures Incidents for Selected Carriers
Component
Bearings
Total incidents: 35
Bearing No. 1
4
Bearing No. 2
4
Bearing No. 3
15
Bearing No. 4
6
Gearbox Bearing
6
Airfoils
Total incidents 80
Fan Blade
9
5th Stage compressor blades
3
7th Stage compressor blades
3
8th Stage compressor blades
10
13th Stage compressor blades
4
15th Stage compressor blades
1
1st Stage HPT blades
37
2nd Stage HPT blades
8
1st Stage HPT vanes
1
2nd Stage HPT vanes
4
Cases
Total incidents: 13
Fan case
4
Intermediate case
1
Diffuser case
4
Combustor case
2
Exhaust case
2
Fuel / Oil Systems
Total incidents: 84
Fuel filter
28
Fuel Line
9
PS-4 Tube
8
Oil contamination
5
Oil cap missing
12
Oil breather tube packing
3
Oil filter
19
Control and Accessories
Total incidents: 366
Fuel control
47
Fuel control/fuel pump
25
Fuel Pump
57
Fuel Pump Sheared shaft
13
Engine vane controller
82
Engine Electronic control
1
TT2 sensor
22
Thrust control computer
1
Pressure Ratio Bleed control
32
Bleed converter valve
19
8th Stage Bleed valve
3
15th Stage Bleed valve
5
Flapper valve
1
HP Butterfly valve
1
TCC control valve
1
N2 Tech generator
7
Constant Speed Drive
5
Main Gearbox
2
Aux Gearbox
1
Angle Gearbox
13
Angle Gearbox Coverplate Stud
9
Hydraulic Pump
7
Fuel/Oil Cooler
7
Oil pump
5
Other
Total Incidents: 254
The EVC removals recorded 82 failure incidents over the observation period which represented 22 percent of all controls and accessories incidents. The following table reviews the impact upon aircraft operation resulting from EVC failures. The most frequent impact was to create engine compressor stalls with resultant shutdowns or flameouts, occurring during in cruise operation and turbulent weather conditions. Engine flameouts also occurred when initiating a descent flight profile.
JT9D Engine Vane Controller failure incidents for selected carriers
Engine Operational Characteristic
Take-off
Climb-
out
Cruise
Descent
Approach
Total
Flame out
1
4
5
7
1
18
Shut-down
–
1
2
1
–
4
Stall/ Shut-down/ Flameout
2
13
21
7
–
43
Smoke Cabin Fume
3
3
2
–
–
8
RPM Spool-down/ Surge
1
2
3
–
–
6
Oil out bleeds/ High consumption
3
–
–
–
–
3
Total
10
23
33
15
1
82
Due to the high failure rate of the Hamilton Standard EEC-103 engine electronic control unit for sufficient engine vane, bleed and fuel control of the JT9D-7R4 engines noted above, the quality engineer wants to further analyze the contribution in failures of circuit boards installed in the engine control units.
Variation in any process is the enemy of quality. So, he/she measures the response times in micro-seconds of circuit boards, taken as samples from production. The corresponding response times are presented in the following table:
Sample No.
Response time of 1st Unit (microseconds)
Sample No.
Response time of 2nd Unit (microseconds)
Sample No.
Response time of 3rd Unit (microseconds)
1
9.8349
31
9.5921
61
10.9865
2
10.6062
32
10.6384
62
10.1667
3
10.4309
33
11.9476
63
10.648
4
10.8353
34
9.3378
64
10.8655
5
11.5492
35
10.7256
65
9.6441
6
11.3765
36
9.149
66
9.4935
7
11.9622
37
11.2958
67
9.5938
8
11.5008
38
10.0071
68
9.9451
9
10.1507
39
11.4671
69
9.9249
10
10.6148
40
9.5617
70
10.7467
11
11.5669
41
10.0719
71
11.0077
12
11.7215
42
9.9877
72
11.3191
13
10.2932
43
11.0624
73
9.699
14
10.0313
44
9.8879
74
11.8654
15
10.6968
45
11.6696
75
10.7268
16
10.9467
46
9.4783
76
10.5606
17
9.8886
47
9.5816
77
11.7527
18
11.9856
48
10.677
78
9.5487
19
10.6817
49
9.0824
79
10.3044
20
10.3432
50
9.0276
80
11.0843
21
11.6352
51
12.5914
81
10.8585
22
11.3068
52
12.0844
82
10.9107
23
11.3547
53
10.1885
83
11.2644
24
11.3604
54
12.0681
84
11.1816
25
11.8867
55
11.6156
85
10.0491
26
10.1766
56
11.2811
86
11.2176
27
11.2015
57
10.5429
87
13.1636
28
11.7641
58
13.0865
88
11.4385
29
12.553
59
12.6777
89
13.0689
30
12.8544
60
12.8306
90
13.6622
You as a member of the quality control department, you are asked to write a report that on the findings.
Plot the frequencies of failures of the JT9D engine general components and the frequencies of failures of engine subcomponents in cascading Pareto Charts to perform a Pareto analysis, identifying according to the 80/20 rule the most critical engine components as well as the most critical aircraft operation phases for failures of the engine vane controller.
Compute the values of mean and standard deviation of the above recorded sample date. Also, compute the values of and . How many parts of every 100 (percentage) produced items would you expect to scrap for acceptance rates: respectively? Produce a histogram using the given data and show the above acceptance rates. What would you have to do to bring the scrap rate at the acceptance rate of down to the scrap rate of . Compared with the curve produced before, what would the new histogram have to look like?
Make recommendations and support your arguments with logical assumptions based on the frequencies of engine components failures.
This project has to completed, printed and handed in for marking prior to the class on April 11th 2017. It must reflect professional standards. Marks will be deducted per day for projects handed in after this date.
Review
Population
The entire group of members of whatever it is your counting. If you are talking about a production process, the population is all the items that you produce using that process.
Sample
A subset of the population: In most cases, you can’t test all the items you produce so you have to test a representative sample.
Mean
The arithmetic average of all the values that you measure for a given test: You calculate the mean by adding up all the individual measurements and dividing by the total number of measurements
Range
From the largest to the smallest. For instance a population of between 20 and 100 would have a range of 80.
Standard Deviation
Is a measure of the range of a variation around the average of a group of measurements. It’s the average distance that each measurement is from the mean of all the measurements. The Greek letter sigma (σ) refers to the standard deviation of an entire population.
Classroom Exercise:
This exercise will explain the steps necessary to calculate the standard deviation:
1/ the following five measurements were obtained from a process:
6, 8, 7, 9, and 10
2/ Add all the measurements up and divide by the number of measurements
6+8+7+9+10 = 40 divided by 5 = 8. This is the Mean.
3/ Take the difference of each measurement from the mean
6-8 = -2
8-8 = 0
7-8 = -1
9-8 = 1
10-8= 2
The differences are therefore: -2, 0, -1, 1, and 2
4/ Square each of the differences
-2^2 = 4
0^2 = 0
-1^2 = 1
1^2 = 1
2^2 = 4
If you square a negative number it becomes positive.
5/ Add each of the squared values together
4 + 0 + 1 + 1 + 4 = 10
6/ Take the total at step 5 (10 in this example) and divide by the number of samples minus 1 (subtract 1) ( this is the sample factor)
10 Divide by 4 = 2.5
7/ Take the square root of 2.5 = 1.58
This is the calculated Standard deviation. The standard deviation is a means to examine the results of a process.
68.27 % of the population should be within + 1 standard deviation
95.45 % of the population should be within + 2 standard deviation
99.73% of the population should be within + 3 standard deviation
From this process, it is now possible to make use of the obtained figures.
8/ Plot the figures on a graph with the average shown.
9/ calculate the + and – for one, two and three standard deviations. Use the figures as shown.
1σ = 1.58 + 8 = 9.58, 8-1.58=6.42
2σ = 3.16 + 8 = 11.16, 8-3.16 = 4.84
3σ = 4.74 + 8 = 12.74, 8- 4.74 = 3.26
In this instance, let us assume that the ideal is the same as the average 8
10/ Add the above lines for +1, 2 and 3 standard deviations to the graph
Histogram Construction:
Using the sample data given and based on the sample size choose the number of intervals for the histogram:
Size of Sample <75, then number of intervals 5-7
Size of Sample 75-300, then number of intervals 6-10
Size of Sample >300, number of intervals 10-20.
Or use formula: number of intervals=square root of sample size.
Count number of data in each interval.
Plot frequencies (i.e. number of data for each interval) to obtain the histogram, label appropriately, show mean and one, two and three standard deviation levels.
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