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APMS developed several tools for Flight Operations Quality Assurance (FOQA) programs in response to user needs studies at participating air carriers, most of which have become vendor standard.
   
The APMS team has also been working on tools to broaden and deepen the analysis of flight data. Flight data may identify precursors of incidents and accidents and the contextual factors such as weather and air traffic that might make consequences of deviations more and less likely.
   
  • Pattern Search enables an analyst to search any portion of a database for a specified pattern of flight parameters (or time sequence of patterns). The user specifies the criteria defining an event, such as an unstable approach, and the search provides a list of flights fitting the pattern.
  • Phase of Flight Reports present key descriptive statistics and distributions by phase of flight and user-selected sample characteristics.
  • Morning Report of Atypical Flights uses multivariate cluster-analysis to group flights by similarity along flight signatures derived from parameter values, calculates an atypicality score for each flight, and provides a description of what makes targeted flights atypical each day that data are uploaded
  • Data Integration allows the linkage of a flight to the weather or air traffic data at any time within the flight, while screening from the display time and date information that would re-identify the flight to the analyst. This information is immediately useful to examining the context of a flight identified as atypical or having an exceedance.
Boeing 777 cockpit image
   

Extracting this information is extremely difficult by manual analysis or calculation -- searching for key values through terabytes of data or calculating values over dozens to hundreds of parameters measured many times per second. Machine processing is necessary to drastically reduce the human labor required, or normative and contextual analyses are very unlikely to be undertaken in a commercial environment. These analyses will complement the information from exceedances, providing greater insight into their operational significance and enabling the discovery of unexpected events.

   
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