Final Research Methodology
Prevention FOD on Runways
RSCH202- Intro to Research Methods

Muhd Ridwan Bin Abdul Malek
Embry-Riddle Aeronautical University

Contents
Introduction3
A Brief Literature Review3
Research Questions4
Theoretical Framework4
Hypotheses5
Study Design5
Population and Sample 6
Variables and Measures6
Data Collection6
Data Analysis Methods 7
Actual Sampling and Data analysis8
Reference 9

Introduction
Foreign Object Debris (FOD) has been a problem in the aviation industry. It is important to prevent FOD as it can affect the economic cost for airlines (McCreary, 2008), severe damages on turbine blades (Mehdi 2014) and shallow indentions can developed to a fatigue crack (Chen, 2001). The focus will be on preventing FOD on runway as it is the most important area to monitor in terms of safety aspects (McCreary, 2008). It is also important to examine the preventions in placed and its effectiveness within the organization.
A Brief Literature Review
Previous research done to prevent FOD has shown that there are several preventions lay out in the aviation industry. Some of them would be by training, inspection as well as maintenance (Bachtel 2008). For training, workers in the airport should receive training in identification as well as removal of FOD. This includes the potential consequences of overlooking it. Training is effective when the importance of safety is emphasis to personnel and passengers, direct and indirect cost of FOD. Procedures should be present in order to remove or eliminate FOD when needed. It can be reinforced by using posters and signs. Everyone should undergo recurrent training to maintain the awareness of FOD. For inspection, airline personnel should participate with the airport staff in daily runway inspection. With this, it encourages effective communication between airport and airline. Lastly, FOD control can be done my maintenance. There are several maintenance methods such as sweeping, magnetic bars, rumble strips and FOD containers.
In addition, there are a widespread FOD management program, and accumulates current practices, techniques, and lists of tools available for use or those currently being used by airports for FOD inspections (Prather, 2011).
Finally, there are advance FOD detection system such as Trex Enterprises' FOD Finder, QJnetiQ's Tarsier, and Xsight's FODetect (Bough, 2009). These systems use sensors, cameras and radar to find FOD such as small discarded metals, bolts and nuts, rocks and other objects that can cause runway accidents. The benefits of using these detection systems are that they can spot FOD immediately when it is present on the runway and the high reliability it has using a computerised system. However, some airlines could argue that these advance system can be too expensive. Bough stated that a properly installed FOD detection system can save an airport US$1 million a year in runway closures. Therefore this shows that money is well invested on FOD detection system.
As all these preventions had been already in place, the author will determine if they are effective and has it reduced the number of FOD incidents or accidents for the past few years.
Research Questions As stated, the author will look into all these prevention methods and determine if they are effective. This can be done by comparing and analyse using FOD detection system and humans detecting FOD on runways. Bangkok's Suvarnabhumi International Airport's Runways (LLC 2012) is an example where FOD detection system is already operated. In addition, variables such as the airline personnel and airport staff can be tested if they are familiar with the procedures and awareness of FOD. This is because humans play an important role as they need to comply with procedures and trainings.
Theoretical Framework The dependent variable of the amount of FODs is influenced by an independent variable of advancement of detection system or humans. Those airlines that are not capable to invest on the advancement of detection system will be disadvantage. Their runways would rely on the prevention such as human related operations or procedures. Thus, these lesser-budget airlines can be in a disadvantage situation. In addition, the effectiveness of FOD management program, such as the one created by Daniel Prather in 2011, is influenced by the compliance of the users (airport staff/personnel). The FOD management program is good if the users follow it. Therefore, supervisors play an important role to ensure that users do not violate the program creating the possibility of the presence of FOD. In summary, money and humans are the main factor of ensuring the effectiveness of the programs and systems in placed to prevent FOD.
Hypotheses
Hᵢ- There is a significant difference in detection of number of FODs between using advancement of detection system and humans.
Hₒ- There is no significant difference in detection of number of FODs between using advancement of detection system and humans.
Research Methodology
Study Design A cross-sectional study will be carried out in this research project. It will be conducted in a period of 30 days. Field experiment is the most suitable to test two independent variables (advancement in detection system and humans) at a selected runway in Bangkok's Suvarnabhumi International Airport. The selected detection system will be FODetect from Xsight systems. FODetect has excellent detection capabilities that use a unique hybrid optical-radar sensing technology. This allows high performance FOD and bird detection as well as providing constant command over airport runways. There will be two situations: Airport personnel or staff conducting FOD check on runway and using FODetect to detect FOD. This experiment will be carried out for one hour per day. It will be a parallel experiment conducted. Field experiment is selected because nothing but the presence of advancement of detection system is manipulated, with all activities occurring in the normal and natural work environment. However, it is possible that some other factors might affect the findings. For example, one of the areas can be the compliance of the users (airport staff/personnel).
Population and Sample
The experiment will be conducted throughout 30 days, for each day there will be 2 methods to be carried out. Therefore there will be a population of 60 results. According to Raosoft, recommended size will be 53 for 95% confidence level. However, the duration of the experiment may extend to achieve better results.
Variables and Measures
The independent variables will be a selected FOD team of ten airport staff and FODetect with an operator overviewing. The dependant variable will be the amount of FOD which will be counted as an item. Debris will be whatever things which do not belong to the runway, for example, bolts, nuts, birds, broken parts, and broken pavements which are any debris that can cause harm or damage to airplanes. A supervisor will be assigned to overview this experiment and decide if the items detected are FOD. This is to ensure consistency. This experiment will be conducted back to back starting with FODetect then followed by the FOD team. FODetect will scan through the runway and identify the amount of debris detect. However, the FOD will not be removed until the FOD team has done their FOD check throughout the runway. Therefore each scenario collects their own data. This experiment will be within an hour each.
Data Collection
The assigned supervisor will be collecting the data from the representative for each scenario. All data will be input on a common Excel sheet everyday within 30 days. As it is conducted once per day, only two data (amount of FODs) will be input in the Excel each day. A leader will be monitoring the FOD on the ground to ensure compliance and reliability.
Data Analysis Methods
Independent sample T-test will be used to analyse the results. It is to examine the differences in each situation. It will be measuring any significant differences in the means for two groups (FODetect and Humans). In addition, it also measures the mean difference between two groups.

Actual Sampling Day | FODs by FODetect | FODs by Humans | 1 | 9 | 5 | 2 | 8 | 6 | 3 | 6 | 7 | 4 | 7 | 5 | 5 | 8 | 5 | 6 | 5 | 5 | 7 | 7 | -Data for the first 20 days of experiment. | -FODs are counted as an item. |

-Data for the first 20 days of experiment. | -FODs are counted as an item. |

4 | 8 | 9 | 5 | 9 | 7 | 4 | 10 | 7 | 5 | 11 | 7 | 3 | 12 | 6 | 4 | 13 | 8 | 5 | 14 | 9 | 6 | 15 | 5 | 7 | 16 | 6 | 5 | 17 | 7 | 6 | 18 | 8 | 6 | 19 | 7 | 5 | 20 | 8 | 5 | | | |

Data analysis t-Test: Paired Two Sample for Means | | | | | | | FODetect | Humans | Mean | 7.2 | 5.15 | Variance | 1.43157895 | 0.9763158 | Observations | 20 | 20 | Pearson Correlation | -0.0712301 | | Hypothesized Mean Difference | 0 | | df | 19 | | t Stat | 5.71175159 | | P(T<=t) one-tail | 8.3209E-06 | | t Critical one-tail | 1.72913281 | | P(T<=t) two-tail | 1.6642E-05 | | t Critical two-tail | 2.09302405 | |

T stat is larger than the appropriate table value (1.729). This means that the difference is statistically significant. The null hypothesis must thus be rejected and accept the alternate hypothesis. Therefore there is a significant difference in detection of number of FODs between using advancement of detection system and humans.
References
1. Iain McCreary (2008). The economic cost of FOD to airlines. Insight SRI Ltd. Retrieved from “http://insightsri.com/system/files/The+Ecomonic+Cost+of+FOD+-+Jul08.pdf”. 2. Marand Seyed Masoud, Rahmani Kh, Tajdari Mehdi (2014). Foreign object damage on the leading edge of gas turbine blades. Aerospace Science and Technology, ISSN 1270-9638, 02/2014, Volume 33, Issue 1, page 65. 3. Chen, Xi 1 and Hutchinson, John W. (2001). Foreign object damage and fatigue crack threshold: cracking outside shallow indents. International Journal of Fracture (Netherlands), volume 107.1, pages 31-51. 4. Brad Bachtel (2008). Foreign Object Debris and Damage Prevention. Retrieved from,“http://www.boeing.com/commercial/aeromagazine/aero_01/textonly/s01txt.html”. 5. C. Daniel Prather (2011). Current Airport Inspection Practices Regarding FOD (Foreign Object Debris/Damage). Transportation Research Board. 6. Paul B rough (2009). Advance for FOD detection. Airport Business, volume 23, page 19. 7. PR Newswire Association LLC (2012). X-Sight Systems Automated FOD Detection System - to Keep Ultimate Safety in Bangkok's Suvarnabhumi International Airport's Runways. PR Newswire Europe Including UK Disclose.