With more data, FAA officials say they can build an algorithm to predict when runway conditions will lead to brake failure during landings.
What if you could tell that a plane’s brakes were about to fail based on conditions on the runway? What if just such a plane was about to land at your airport? What if you could do something about it?
The William J. Hughes Technical Center Airport Technology Research and Development division—an office under the Federal Aviation Administration—has been collecting data on this problem and plans to build a machine learning algorithm that can predict braking failures due to degraded runway conditions, such as a wet or contaminated tarmac.
But before the office can build that algorithm, it needs more data.
The Airport Technology Research and Development office maintains a database of incidents that occurred at or around airport reported through five federal agencies: the Aviation Safety Reporting System, National Transportation Safety Board, FAA Runway Safety Office Runway Incursion Database, Pilot Deviation System, and Vehicle/Pedestrian Deviation System.
But that data won’t be sufficient for a workable predictive analysis, according to a market survey released Monday. In order to build a reliable algorithm, FAA will need “access to a large volume of “aircraft landing data and the associated external contributing factors.”
The survey seeks feedback on the types of data available on:
- Raw aircraft data: Aircraft type, gross weight, flap position, touchdown ground speed, landing time and time zone, ICAO Airport Designator, and runway of landing.
- Processed aircraft data: Braking friction calculated from aircraft raw data for each third of the landing.
- External weather data: Precipitation type, precipitation intensity, air temperature, air pressure, humidity, pressure altitude, wind speed, and wind direction.
FAA is looking for at least two years of landing data—going back to at least July 1, 2017—from a variety of global airline carriers, landing conditions and plane conditions.
Once the FAA has enough data, the agency intends to build an analytics engine to “identify clusters and branches of relationships between aircraft data and external contributing factors—i.e. weather and runway condition data—to predict braking performance.”
“The ultimate goal is to determine and recommend actions that prevent or mitigate incidents and accidents in advance of their occurrence,” the notice states.
Depending on feedback from the survey, FAA plans to issue a one-time solicitation to acquire the data from a third-party source.
Responses to the survey are due by Aug. 5.
Editor’s Note: This article has been updated to clarify the focus on runway conditions.