MACHINE LEARNING FOR PREDICTIVE MAINTENANCE
We're collaborating with LGM Aviation on an exciting project to aggregate and analyse commercial aviation data. We're looking specifically at the aftermarket segment of aircraft repair and maintenance where an operator might, for example, have little visibility on which essential components for a given aircraft type are most likely to fail. Our aim is to help predict such events and thereby help airlines avoid costly Aircraft on Ground (AOG) incidents.
We're building a comprehensive database of component reliability information for the Airbus A320 and Boeing 737 families initially. We're working with a team of data scientists to map both this and more complex live aircraft data to structures well suited to interrogation by intelligent Machine Learning algorithms. Our aim is to build a predictive aircraft maintenance platform for the commercial aviation aftermarket.
If you're interested in collaborating on this project (especially if you're an airline, MRO, OEM or asset owner) please get in touch.
MODELLING FUTURE AIRSPACE INTEGRATIONS
We're in the early stages of a project looking at the broad range of challenges and pressures facing airspace in the future. The integration into already crowded airspace of delivery drones and air taxis for example, will require both careful modelling, perhaps some new approaches to air traffic monitoring and management and pilot programmes to test new concepts.
We've started to collect small samples of ADS-B data from a complex part of UK airspace and are attempting to use NASA's TRAC software to visualise it and model the impact of introducing new categories of aircraft.
We're also conducting wider research, looking at the variety of initiatives underway worldwide relating to airspace innovation - from the UK's Future Airspace Strategy and Uber's Elevate initiative to NASA's Urban Air Mobility (UAM) Grand Challenge - there's a lot of work underway already, bringing together Regulators, Air Navigation Service Providers (ANSPs) aircraft manufacturers and operators.
If you're interested in collaborating on this project please get in touch with us here.
SELF-DRIVING CARS AND AUTONOMOUS AIRCRAFT - CROSSOVER TECH
It's no coincidence that Sebastian Thrun, CEO of Kitty Hawk (backed by Google co-founder Larry Brin) has led one of the expected frontrunners in the nascent eVTOL aircraft market. Thrun was previously in charge of Google's secretive 'X' skunkworks and led the company's self-driving car project there. He also founded Udacity who offer some of the world's most popular and respected self-driving vehicle 'Nano-degree' e-learning programmes.
Udacity now also offer a range of e-learning programmes for 'Flying Car and Autonomous Flight Engineers' - and here we can see a great deal of crossover in the skills and technologies required to engineer both air and ground-based craft.
From knowledge of programming languages like Python and C++ for motion planning and vehicle control to the architecture of the systems required to enable a vehicle to perceive its location, predict events, plan routes and make control inputs - many of the skills and sensors behind cars that can drive themselves will also be found in the autonomous aircraft of the future.
EVTOL / urban air mobility 'DEEP DIVE'
Our first in-depth research report will take a close look at the Urban Air Mobility (UAM) market, with specific focus on eVTOL.
The flow of capital to the sector is examined and opportunities identified - from vertiporm real estate plays to charging infrastructure roll-out, if the growth of the sector plays out as some analysts predict there could well be a lot of money to be made, and lost.
If you'd like a copy of the report or are interested in gaining bespoke insights into the topic then don't hesitate to get in touch.