We have always put our effort into aggregating and commenting on all the news relevant to the FOD detection industry, and this has proven to be a useful resource for those in the aviation sector who are concerned with FOD. Keeping up-to-date is great, but it’s also nice to have a reference section where people can quickly get answers to some frequently asked questions e.g. where are the current FOD systems deployed? How much can a FOD system reduce risk on a runway? How do the systems compare on features?

Over the next few months I plan on answering some of these questions (and more) by adding dedicated pages to the site, this is of course in addition to keeping the home page up-to-date with all the latest news. Today, I have launched a beta version of the FOD Risk Calculator, originally a downloadable spreadsheet, now an interactive part of the site. It allows anyone to quickly compare FOD systems using parameters such as detection time and probability of detection, or to compare any of the systems against manual FOD checks.

The 2nd page to be added will be an interactive map showing the current deployment of FOD systems (sales only, not trials or demos), along with details of the number of runways covered etc. I’m still working on this page, it should be up and running within the next couple of weeks.

The pie chart below shows the location of visitors for the last month. So, what can we tell from the data? for one thing the data is skewed by visits from the FOD detection system vendors themselves. This might seem odd, as there are only 4 vendors, but you have to remember that there are also less than 10 customers worldwide, so it’s not actually very surprising. Of the top 5 countries, US, UK, Israel, China and Singapore, all but one (China) are home to a FOD detection vendor.

Even with the skew, it’s clear to see where most of the interest in FOD detection comes from, and it’s the US. This should come as no great surprise given the FAA circular, and the availability of FAA funding. Let’s just hope that we start to see some of this interest turn into sales sometime soon!

If a system takes 6 minutes to detect an item of FOD, and the next aircraft is due in 4 minutes, then the FOD detection system is completely ineffective at reducing risk.

It’s the sort of argument that people in marketing dream of, not only does it appear to make a lot of sense, but FOD detection time is easy to quantify, and therefore it’s easy to compare across the various systems. Unfortunately, it does not stand up to any form of rigorous analysis. So, let’s take at look at this in more detail, the first thing we need to do is to define the risk from FOD.

What is risk?

It’s often very difficult to quantify risk in any meaningful way, but with FOD it’s actually quite easy. The risk posed by FOD is proportional to the time the FOD object spends on the runway surface (or other airfield surfaces). Of course, the risk from an item of FOD will also depend on exactly where the FOD is found, and what the object is, but, with a large enough data set these two factors should become less important, and then you’re left with the risk from FOD simply being proportional to the time the object spends on the airfield surface.

For example, a single item of FOD on the runway for 1 hour represents the same risk as two items of FOD on the runway for 30 minutes each. It’s even possible to give units, and I’m going to call the units of FOD risk “FOD minutes” (as it’s simply the number of FOD items multiplied by the time on the surface)

An example

Let’s consider a very simple example of a single item of FOD that is on the runway for 3 hrs, and there is no FOD detection system installed. (I’ve assumed the runway is manually checked every 6 hrs, so the mean time FOD will spend on the runway is 3hrs)

• The risk posed by this one item is 1 x 180 mins = 180 FOD minutes

Now let’s add a FOD detection system and see what happens, let’s assume that the FOD detection system has a detection time of 2 minutes.

• The risk posed by this one item is 1 x 2 mins = 2 FOD minutes

But, it gets even better, if there is an aircraft movement every 4 minutes then for any detection time less than 4 min the FOD risk will actually be zero! Unfortunately this hypothetical case is so simple it’s essentially meaningless, this examples makes 3 assumptions:

1. All FOD items originate from the last aircraft that used the runway
2. The risk from FOD is proportional to the detection time
3. The FOD system detects all FOD

Lets deal with each one of these in turn:

1. Assuming that all FOD originates from the previous aircraft is unrealistic, it assumes that there are no other sources of FOD, such as items from ground vehicles, wildlife, litter, broken concrete or tarmac, stones, tools, luggage etc.
2. The risk from FOD is proportional to the time the FOD spends on the surface, this is the detection time plus the retrieval time. The FOD does not magically disappear after it has been detected.
3. No FOD system is 100% effective. Each will have a probability of detection. So there will still be some FOD on the runway which will be found during manual checks.

A better example

Let’s create a more realistic example that includes the probability of detection, retrieval time, and assumes that there are sources of FOD other than the previous aircraft. First we have to generate a hypothetical airport, this airport has the following parameters:

1. 300 Items of FOD are found at the airport each year.
2. It takes, on average, 5 mins to retrieve an item of FOD.
3. There are 6 hrs between manual FOD checks.
4. The average time between aircraft movements is 4 mins.
5. 50% of all FOD found came from the previous aircraft.

Now let us also introduce two FOD detection systems that differ only in their probability of detection and their detection time.

• System A: Detection time = 1 min. Probability of detection = 90%
• System B: Detection time = 7 mins. Probability of detection = 95%

So which system is most effective at reducing the risk due to FOD? Well, the probability of detection differs by just 5%, but the detection time for system B is 600% greater than for system A. Not only is it 600% greater, but it’s also greater than the the mean time between aircraft movements.

So, the answer is obvious, isn’t it? Actually, each system reduces the risk from FOD by almost exactly the same amount, down to around 10% of the original risk when no FOD detection system was installed (System A reduces the risk to 13%, and system B to 11.3%). More information is included in the image below.

Click to Zoom

But what if both systems had the same probability of detection and only differed in their detection time? In this case, where both systems have a 95% probability of detection, system A has now reduced the risk from FOD to just 8.2%, and system B remains unchanged at 11.3%. So yes, detection time is a factor. But, a massive reduction in detection time results in a very small reduction in risk.

So, why don’t all the FOD detection systems just reduce their detection time to less than the mean time between aircraft movements?

The fact is that you don’t get something for nothing, and this is true for detection systems. And this is where it starts to get interesting. Each of the systems can probably reduce their detection times, if they wished, but, it would come at a cost, and that cost could be a reduction in probability of detection . And we’ve just seen an example where a system that had a smaller detection time (by 600%), had that advantage wiped out by a system that had a better probability of detection, by just 5%!

This is best explained with an example. Consider a detection system (FOD or not, it doesn’t matter) based on a visible camera system. The system makes detections based on information it receives, and that information is in the form of light entering the camera. Less light, less information, reduced probability of detection. So, what happens when it gets dark? One of two things can happen:

1. The camera collects less light, and the probability of detection reduces
2. The camera stares for longer, i.e. it collects light for longer, and the probability of detection remains constant.

There is a FOD detection system on the market that is based on a visible camera system, and the detection time does indeed increase as it gets dark. Why did they choose to follow option 2 above? why not just follow option 1 and allow the probability of detection to decrease. It’s simple, they realise that probability of detection is far more important that detection time.

So, does this mean that systems with a longer detection time are better?

No, I wish it were that simple, but it’s impossible to compare the trade-off between probability of detection and detection time across systems that are based on different technologies. It’s quite possible that a system based on one form of technology will have a better probability of detection, and a shorter detection time.

Does a reduction in detection time always result in a decrease in probability of detection?

No, it’s possible to reduce detection time while keeping probability of detection constant. When you reduce detection time you’re essentially degrading the quality of the data used to determine the detection, you can still maintain the probability of detection by increasing the system’s sensitivity, but again, you don’t get something for nothing. Increasing the sensitivity will lead to an increase in false alarms, and we’re not talking about a few percent. An increase in the false alarm rate is in itself not a problem, if it means maintaining the probability of detection then you’re still reducing the risk on the runway, which of course is the goal. But, if the false alarm rate is so high that the operator starts to ignore the alarms, that’s when the risk on the runway starts to increase again.

So which system suffers from these trade offs between detection time, probability of detection and false alarm rate?

It doesn’t matter if the FOD detection system uses cameras, radar or lidar, the trade-offs still exist. Actually it doesn’t matter if it’s a FOD detection system or not, it’s fundamental to all detection systems. Radar, cameras (visible, infrared), lidar, human beings, sniffer dogs, or squirrels looking for nuts on the forest floor, they are all detection systems and they all suffer these issues. Lets consider the current FOD detection system used in every airport today, the human being, he drives down the runway at a particular speed trying to detect FOD. Let’s reduce his detection time, it’s easy, ask him to drive twice as fast, unfortunately he now has 1/2 as long to look at each object. So what’s the trade-off in this situation? Typically he will find less FOD (the probability of detection will fall), but what if he has been given strict instructions to find the same amount of FOD (i.e. maintain the probability of detection),  well, he can do that, but  the amount of false alarms will rise. It’s a silly example, but it proves the point.

So why do some vendors still insist that their system is better just because they have a shorter detection time?

Unfortunately, it’s our fault. We like comparing numbers, it’s easy, system A has a shorter detection time than system B, therefore I’m going to buy system A. The best analogy I can make is with digital cameras. The number of megapixels a camera has is still used by many people as the factor that most influences which camera they buy. It’s easy to compare this parameter across various cameras. But, much like detection systems, there is a trade off, if the digital camera sensor size remains constant, then as the number of pixels is increased, each pixel has to be made smaller, and unfortunately this tends to increase the noise in the system, and this can result in reduced image quality. And comparing the image quality from different cameras is much harder than comparing the number of megapixels they have. It’s very similar to FOD detection systems, comparing detection time is easy, but comparing the probability of detection, or false alarm rate is not.

To sum up, I’m not saying that detection time is not important, I’m just saying that it’s not as important as it would first appear (and there are far more important parameters to consider). Always remember that the goal of a FOD detection system is to reduce the risk from FOD, it’s not to have the shortest detection time, or the lowest number of false alarms. And to the person (who didn’t  leave their name, position, company, or a usable email address) who asked “Did you neglect the timing requirement[detection time] when you did your system design???[sic]”, I have one question for you, “did you only consider the detection time when you designed/bought your system?” if the answer is Yes, then all I can say to you is “Oops”

I disagree with you, how do I comment? Is an anonymous email the best method?

Ummm….let me think about this….no, an anonymous email is not the preferred method. I’m aware that not everyone is familiar with a blog style website, so here are some basic instructions for anyone who wishes to comment on this article.

At the bottom of this article (and every article on this website) is a section entitled Leave a Reply, this is where comments should be left. It’s public, it allows others to see the comments, which I believe is useful, especially if they have similar comments. If someone has an issue with a particular assumption I’ve made, such as “50% of FOD originates from the previous aircraft”, please remember that this is not supposed to represent an average value, or a value for a particular airfield. If you have some real data for the airport values used above then please use the spreadsheet (link at the end of this article), do the calculation for your particular airport, include your system’s detection time and probability of detection and include your results in the Leave a Reply section.

If you discover an error in the calculation itself then let me know in the Leave a Reply section, I’m human, mistakes happen. The spreadsheet is available for anyone to download, it’s completely open, there are no hidden or protected fields. Please feel free to check the calculations yourself (build on the original calculation if you wish, add other airfield surfaces, change the risk for each surface, add a probability of detection for the manual checks etc). I have nothing to hide, and I’m a fan of transparency. If you are really uncomfortable with your comments being public then feel free to use the Contact Form, but my personal preference is that we keep any discussions public.

The Spreadsheet

Since starting to write this article the spreadsheet has grown and become more involved than was originally planned. It now includes the ability to input data for 3 FOD detection systems, and also allows the user to select which action is taken when FOD is detected, i.e. to close the runway and retrieve, or to continue operations and retrieve. The spreadsheet is provided, as is.

Download the Spreadsheet (FOD risk v2)

Click to view full size

I’ve always liked infographics, they convey complex data clearly and quickly in a way that a report, or a table simply cannot manage.  After watching the recent TED talk by David McCandless I decided to generate a simple infographic that included the cost of a FOD detection system alongside some other aviation costs. I’ve not included actual figures on the graphic as the costs are so variable that it would not be worthwhile.

I plan on generating a new infographic over the next few weeks that will be based on the costs detailed in the Insight report (The economic cost of FOD).

Download the infographic as a PDF.

…well, there isn’t, but I firmly believe there should be.

I’ve just read through the new FAA draft Circular on FOD Management, and it discusses the fact that it’s the responsibility of everyone to report FOD if they come across it. I believe that if you want people to do something, especially if it’s not their main role, you have to:

• make the function quick and easy to perform, and
• offer an incentive.

And let’s get one thing straight, asking a contractor who’s working airside to visit an office on the far side of the airfield to locate and complete a FOD reporting form does neither of these, it’s definitely not quick, and there’s no incentive. Actually it’s more likely to get them into trouble with their boss as they’d have to explain why they’d not been doing their job for the last 30 minutes!

The first thing you have to do is to give the person who found the FOD a method of recording the event then and there, because if they plan to leave it until the end of their shift then it’s not going to get done at all, and I’m not talking about a new device for them to carry around all day, because they won’t carry it. You have to take advantage of the fact that it’s very likely that they’re carrying a small recording device with them anyway, and yes, if you haven’t already guessed from the mock poster above, I’m talking about a smartphone.

There are 3 reasons why  smartphones are perfect for recording FOD finds:

• they have cameras
• they have GPS
• they can transmit data

The apps function would be very simple with the help of mobile testing services, take a picture of the FOD, select a category (wildlife, tools etc), and assign a risk (low, medium, high), that’s it. The image would be tagged with the location via GPS,and the data would then be sent to a central database. Once the FOD find has been recorded, the app could then give the location of the nearest FOD bin, or supply the phone number of the FOD manager.

Provide an incentive

Even if something is quick and easy to do people still need an incentive to do it. If the user who downloads the app also has to register, then any FOD finds they record will be registered against them, and then it’s simply a case of offering some form of reward, e.g. entry into a monthly prize draw. The more FOD they record the more likely they are to win.

Make it global

One of the advantages of this concept is that once a user has downloaded the app and registered as a user, it could be used on any airfield in the world, the GPS data would be all that’s required to identify the airfield, and this location information would then supply custom information back to the user i.e. the phone number of the FOD manager and the location of the FOD bins (or any FOD procedures that are unique to the airfield)

It’s all about the sharing

Where would all this FOD information go? I would strongly suggest to a single, centrally (FAA?) managed database. Airports could be given access via a website to the data collected from 100’s of airports, this data could then be used to generate better targeted FOD procedures.

If you have any thoughts on this concept then please leave a comment in the comments section below, or get in touch via the contact form.

Although the Tarsier and iFerret systems both employ different detection technologies (Radar and camera respectively), they are still both bound by the same requirement, they both need clear line-of-sight to the runway, and must achieve this without breaking the rules imposed in AC150/5300-13, specifically, the Primary Surface, the Transitional Surface and the Taxiway Object Free Area Width.

The Primary Surface: The primary surface is an imaginary surface longitudinally centered on the runway.  The elevation of any point on the primary surface is the same as the elevation along the nearest associated point on the runway centerline.  The primary surface width is 1000 feet and centered on the runway center.

The Transitional Surface: The transitional surface begins at the outside edge of the primary surface at the same elevation as the runway.  The surface rises at a slope of 7:1, up to a height 150 feet above the highest runway elevation.

The Taxiway Object Free Area Width: The area on the ground centered on the taxiway provided to enhance the safety of aircraft by having to be free of objects. For group VI  aircraft this width is 386 feet.

Click image to zoom

What these rules mean in reality

The image to the left demonstrates what these excluded areas mean on a real airfield, in this case just two runways and the surrounding taxiways at Hartsfield–Jackson International (ATL). It should be noted that the shaded area represents only the Primary Surfaces and Taxiway Object Free Areas, it excludes the Transitional Surface.

In reality these rules result in much of the airfield being out of bounds to non-frangible towers. In order to demonstrate the difficulties I’ve performed a Site Design for deployment of both the Tarsier and iFerret systems at ATL.

9R/27L at ATL (Click image to zoom)

Runway 9R/27L

The building rules described above exclude any tower construction to the North of this runway, so both iFerret and Tarsier would have to install to the South. A runway of this length would typically require two Tarsier units, placed 1/4 way from each runway threshold. Tarsier units are typically placed 740 feet from the runway center line. Using this information it is possible to calculate the Tarsier unit locations required in order to provide full coverage. I’ve also shown in the image the approximate coverage (shaded areas). In this example I’ve shown the Tarsier system to have a coverage of around 2600 feet, however it should be noted that detection performance would actually degrade with range.

Tarsier locations (Click image to zoom)

The iFerret towers at Changi International are located at 820 feet from the runway center (a little farther away than the Tarsier towers), and are separated by 1115 feet. Using this data it is possible to perform a site design  for iFerret for this runway. This is shown in the image to the left, along with the approximate coverage. The iFerret units are assumed to operate out to a range of  990 feet (as described previously). As can be seen, 9 iFerret units are required in total, but this does not provide full coverage since there is a small gap in coverage between iFerret units 6 and 7 that is caused by the taxiways leading to the South cargo ramp.

iFerret locations (Click to zoom)

As with Tarsier, detection performance also degrades with range for iFerret. Coverage does not end at 990 feet, so the system would still cover this area but at a reduced detection capability.

These simple examples demonstrate  typical installation considerations for both the Tarsier and iFerret systems. In these examples both systems provide almost full runway coverage. However these examples used a relatively simple runway configuration, with a relatively uncluttered taxiway system to the South. In the following example I’ll demonstrate one of the main problems facing the installation of the Tarsier and iFerret systems on runways with complex layouts.

High density of taxiways

The building restrictions imposed by having two parallel taxiways on one side of a runway can mean that the nearest a tower could be built is over 1000 feet from the runway center. This range is much farther than the preferred install ranges of both the Tarsier and iFerret systems (740 feet and 820 feet respectively) and is actually greater than the quoted operating range of the iFerret system. Runway 08R/26L at ATL is a good example of such a runway. Not only does it have double taxiways to the South, but installation to the North is impossible due to a taxiway and runway 08L/26R.

Runway 08R/26L

To the South East is a double parallel taxiway, the closest a tower could be constructed to the SE is 1010 feet. Due to its long operation range (approx 2600 feet) , Tarsier could be located at this range, but iFerret (with its quoted range of 990 feet) would be excluded. Even at this range of 1010 feet this would place any tower very close to parked aircraft and terminal buildings, and I have my doubts as to whether Tarsier or iFerret could provide suitable locations to cover this runway.

Unfavorable ground height

The reason that both Tarsier and iFerret prefer to operate at a range of approximately 800 feet from the runway centerline is that this is as close as they can place their sensors to give them line-of-sight to the far side of the runway without breaking the Transitional Surface. In order to do this they need to place their sensors on towers that are around 30 feet in height. This height is not measured from the ground, but from above the elevation of the nearest associated point on the runway centerline. In other words, if the ground height at the sensor location is 10 feet below that of the nearest runway centerline, then the required sensor tower would be 40 feet. As far as I am aware no vendor has as yet used a tower that is greater than 30 feet in height. This may not be impossible, but it has not yet been implemented in practice.

Click to zoom

Runway 10/28 at ATL is a good example of a runway which has very unfavorable sloping ground conditions. In the image to the left you will see just one of the possible tower locations for this runway (this location would be applicable for both Tarsier and iFerret). As you can see from the image the ground appears to fall off quite dramatically as you move away from the taxiway. This situation is repeated for many of the possible locations surrounding this runway.

Summary

The aim of this article is to highlight just two of the factors that have to be considered when installing a fixed FOD detection system i.e. density of taxiways and unfavorable ground height. There are many other factors that have to be taken into consideration by the vendor’s Site Design engineer. By understanding the issues associated with Site Design a potential customer can be an informed customer, who can ask relevant questions, and better understand any potential risks associated with selecting a particular FOD detection system (e.g whether that customer would be the first to purchase a higher tower, or a frangible tower)

The full Site Design for ATL is available to download as a Google Earth file (.KMZ). This contains all the runways at ATL, and not just those discussed in this article.