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Flight Data Monitoring

Flight Data Monitoring. CGAR Conference June 13, 2012. The Team UND Jim Higgins Dana Siewert Gary Ullrich Lewis Liang Brett Venhuizen Karin Hennseleck , GRA Yong Lai, GRA Computer Science Sophine Clarchar , GRA Computer Science. What is Flight Data Monitoring (FDM)?

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Flight Data Monitoring

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  1. Flight Data Monitoring CGAR Conference June 13, 2012

  2. The Team • UND • Jim Higgins • Dana Siewert • Gary Ullrich • Lewis Liang • Brett Venhuizen • Karin Hennseleck, GRA • Yong Lai, GRA Computer Science • SophineClarchar, GRA Computer Science

  3. What is Flight Data Monitoring (FDM)? • The systematic collection of data from onboard flight recording devices • The aggregation of all data into a central data repository • The rigorous analysis of the data to proactively identify hazards currently undetected • The adaption of policies and/or procedures to mitigate or eliminate the risks of all identified hazards

  4. FDM has been implemented at many airlines throughout the world • Usually known as Flight Operations Quality Assurance (FOQA) • Widely accepted as an effective method of proactive hazard identification

  5. If You Can Measure Behavior, You Can Manage It HOMP Begins in September 2007

  6. Safety & Finance: Bristow Aug 2010 Investor Slide

  7. So what is CGAR trying to accomplish with its FDM research? • Bring FDM to General Aviation

  8. Current status of FDM at UND • 62 Cessna 172s Garmin G1000 SD Card • 3 have Appareo Vision 1000 systems • 1 Bell 206 Appareo Vision 1000 system • RFP Discussion

  9. How is the current CGAR FDM grant is overcoming the barriers for widespread deployment of FDM GA?

  10. The systematic collection of data from onboard flight recording devices • Barriers for GA • Recording Technology/Cost • Overcome by G1000 SD card • Flight recorder vendors • Potential mobile devices/handheld units

  11. The aggregation of all data into a central data repository • Barriers for GA • No database/software • Overcome by the development of the NGAFID

  12. Current Status of the NGAFID • Originally had over 80,000 files and 100,000 hours in database • After the correction, we now have 50,468 files in database • Estimated 76.3% are flights (≈38,000 flights) • Defined as indicated airspeed > 30 • Estimated 1.35 mean flight time • Estimated: 51,984 flight hours

  13. The rigorous analysis of the data to proactively identify hazards currently undetected • Two options • Hire a vendor (cost-restrictive for weekend pilots) • Use FDM software (cost-restrictive for weekend pilots) • Overcome by NGAFID toolset (under development)

  14. The adaption of policies and/or procedures to mitigate or eliminate the risks of all identified hazards • Barriers for GA • Not a widespread adoption of SMS (yet) • Hopefully will be overcome by the efforts of other CGAR projects and industry (GA JSC)

  15. A Case Study in how FDM could work in GA • UND Ramp • The UND SMS risk analysis people determined that we were at a heightened risk for an accident or major incident on our ramp • Reasons for this risk: • Congestion • Taxi speeds • Complacency

  16. A Case Study (Continued) • Can this be tracked? • Tracking variables (frequency) • Taxi speed  FDM • Complacency  LOSA/Observations • Congestion  LOSA/Observations • Tracking variables (severity) • Reporting  ASAP • Accident reports

  17. A Case Study (Continued) • Time-of-day example

  18. Other exceedances that have been analyzed • Maximum Altitude • Maximum Bank Angle • Maximum Pitch Angle • VNE

  19. Specific Problems • Aircraft WoW • Aircraft flap setting • Terrain elevation • ARINC 429 data/onboard data • Automated retrieval

  20. Phase III Directions • Formally invite other collegiate fleets to share data • Develop educational tools regarding to use the NGAFID • Develop national FDM database usage and access standards • Open the NGAFID to the flying public • Secure long-term support for the continued operation of the NGAFID

  21. Thank You! Jim Higgins jhiggins@aero.und.edu

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