An award-winning professor at MIT is working to develop a system to spot accident trends in airline flying before the accident happens. MIT’s John Hansman along with colleagues at MIT and in Spain are working on a data analysis detection tool that uses cluster analysis, which is a form of data mining that breaks flights down into series of common patterns and then looks for anomalies in those patterns. Once those outliers are flagged, analysts can further study the data to see if the unusual data is of any real concern.
In a limited form, such analysis is already in use, through Flight Operations Quality Assurance (FOQA) programs in which certain airlines participate. Through FOQA, the airlines gather data, generally mechanical position and performance data, to spot potential problems so they can be prevented before they cause an accident.
