Augmented Sensing in Commercial Aviation

Connected Aircraft

 

Problem

Commercial aviation is by its nature a very data-rich business. All aspects of an average airline flight, from passenger and cargo information, to flight operations data, to maintenance and component health data, are potential sources of value for airlines, which compete in a tight margin business. This data has been collected in some limited form for years, but new aircrafts, such as the Boeing 787, have revolutionized the ability for airlines to start collecting massive amounts of data that previously went uncollected. The 787 collects up to half a terabyte of data per flight from a suite of on-board sensors, including engine diagnostic sensors, brake health indicators, and flight controls movements. The addition of augmented sensing to the aviation business has the potential to reduce airline costs and increase the quality of service to consumers.

 

Current State of the Industry

The industry is currently using this data primarily to predict when components will need servicing, reducing flight delays for maintenance. Sensors onboard the aircraft collect data related to the health of airline components, and feed this data back to the airlines, who can proactively plan when to take a plane out of service, rather than discovering these issues at the gate. This data is also being used to help manufacturers build better parts, and more accurately predict future planned maintenance schedules. They can then build upgraded parts for existing planes in service, reducing maintenance cost and improving the performance of the airplane. One issue with the current state is low-bandwidth available to airplanes in flight, as legacy systems typically only have a bandwidth of 10-15 kbps during flight. Airlines can bypass this currently either by upgrading to higher bandwidth satellite connections for inflight data transmission, or downloading data after a flight when faster ground-based connections are available.

 

 

Future Opportunities

While the industry is primarily using this data for predictive maintenance purposes now, this data could be used in a wider context, using machine learning algorithms. One area where machine learning could improve efficiency is in flight optimization. Each flight generates an enormous amount of data regarding the decisions the pilots make when they fly the plane. While this data is difficult to analyze, given the huge number of variables, including air traffic control instructions, weather conditions, and airplane design, machine learning could help sort through the data. A neural-net style algorithm could analyze all of the available flight data, and build models of the most efficient pilot decisions. These decisions include when to use flaps in landing, what power settings to use, and what cruising altitude is optimal, given the weather conditions. Once this data is analyzed, airlines can communicate new guidelines to pilots to increase the efficiency of their operation.

 

Another application would be to integrate an optimization algorithm into the current predictive maintenance scheduling system. Airlines don’t have every spare part available at every airport they serve, so integrating predictive maintenance data with flight schedules and part availability data could allow an algorithm to determine the most efficient time and location for an airplane to be taken out of service. This would save airlines from having to perform costly repositioning flights as airplanes are flown from a maintenance hub back to where they are needed in service.

 

Finally, airlines could use algorithms in real-time to help pilots navigate the most efficient routes to their destinations. Airlines could take airplane performance data from previous flights and integrate that data with current weather data and air traffic control information to plot the most efficient route for an airplane to complete its journey. This would be especially useful in navigating around storms, since current guidelines instruct pilots to fly well clear of them in a boxy pattern determined before the flight, instead of flying the most efficient route to clear a storm cell as it moves. The addition of Big Data machine learning and optimization algorithms to the new-found wealth of data in the aviation industry could unlock billions in value for the airlines, and provide a better flying experience to the general public.

 

 

 

 

Sources

http://www.aerospacemanufacturinganddesign.com/article/millions-of-data-points-flying-part2-121914/

http://aviationweek.com/connected-aerospace/getting-ready-big-mro-data

http://www.computerworlduk.com/data/boeing-787s-create-half-terabyte-of-data-per-flight-says-virgin-atlantic-3433595/2/

One thought on “Augmented Sensing in Commercial Aviation

  • May 2, 2017 at 6:25 pm
    Permalink

    -_-

    It would have been good to see a discussion about how this would differ from what firms such as GE are doing. That is, how are you addressing point (b) in the solution pitch. It also appears that you don’t address points (c) or (d).
    (c) Detail the design of an empirical demonstration that persuades others of their solution’s commercial value and promise, oriented toward potential investors, bosses, colleagues, and critics of the proposal
    (d) Pilot that demonstration to reveal its plausibility, promise, and appropriable value

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