In Plane View
MIT News (09/12/11) Jennifer Chu
Massachusetts Institute of Technology professor John Hansman and colleagues have developed an airline health detection tool that identifies flight glitches without knowing ahead of time what to look for. The method uses cluster analysis, a type of data mining that filters data into subsets to find common patterns. Flight data outside the clusters is labeled as abnormal, enabling analysts to further inspect those reports to determine the nature of the anomaly. The researchers developed a data set from 365 flights that took place over one month. "The beauty of this is, you don't have to know ahead of time what 'normal' is, because the method finds what's normal by looking at the cluster," Hansman says. The researchers mapped each flight at takeoff and landing and found several flights that fell outside the normal range, mostly due to crew mistakes rather than mechanical flaws, according to Hansman. "To make sure that systems are safe in the future, and the airspace is safe, we have to uncover precursors of aviation safety accidents [and] these [cluster-based] analyses allow us to do that," says the U.S.'s National Aeronautics and Space Administration's Ashok Srivastava.
MIT News (09/12/11) Jennifer Chu
Massachusetts Institute of Technology professor John Hansman and colleagues have developed an airline health detection tool that identifies flight glitches without knowing ahead of time what to look for. The method uses cluster analysis, a type of data mining that filters data into subsets to find common patterns. Flight data outside the clusters is labeled as abnormal, enabling analysts to further inspect those reports to determine the nature of the anomaly. The researchers developed a data set from 365 flights that took place over one month. "The beauty of this is, you don't have to know ahead of time what 'normal' is, because the method finds what's normal by looking at the cluster," Hansman says. The researchers mapped each flight at takeoff and landing and found several flights that fell outside the normal range, mostly due to crew mistakes rather than mechanical flaws, according to Hansman. "To make sure that systems are safe in the future, and the airspace is safe, we have to uncover precursors of aviation safety accidents [and] these [cluster-based] analyses allow us to do that," says the U.S.'s National Aeronautics and Space Administration's Ashok Srivastava.
No comments:
Post a Comment