An airplane’s digital flight-data recorder, or “black box,” holds massive amounts of data, documenting the performance of engines, cockpit controls, hydraulic equipment and GPS systems, typically at regular one-second intervals throughout a flight. Inspectors use such data to reconstruct the final moments of an accident, looking for telltale defects that may explain a crash. More recently, analysts have probed black-box data in an effort to prevent such accidents from ever occurring. Using software tools that can rapidly search data, operators can flag problem areas and determine whether a plane needs to be pulled off the line to be physically inspected, or if there are problems with flight procedures.
Many airlines routinely run software programs to check a plane’s overall health and performance after each flight. However, John Hansman, professor at MIT, says today’s search methods are limited, with operators needing to identify ahead of time which parameters to check. Hansman says this approach may cause analysts to miss key information that would otherwise help airlines improve aircraft safety and operations.
Now Hansman and his colleagues at MIT and in Spain have devised a detection tool that spots flight glitches without knowing ahead of time what to look for. The technique uses cluster analysis, a type of data mining that filters data into subsets, or clusters of flights sharing common patterns. Flight data outside the clusters are flagged as abnormal; analysts can then further inspect these reports to see whether an anomaly is cause for alarm.