SMART ENGINES AND AVIONICS — IOT’S MAIN
ENTRY ONTO AIRCRAFT While we have many airport, passenger and cabin management devices become part of the IoT universe, newer aircraft engines are arguably the main driver of this technology. Newer avionics will be joining this trend as well. The reasons for this are many, but primarily that the economic motives for tracking performance, maintenance and service issues provide the OEMs with motivation to pack in more sensors into their products.
Examples of this include the Pratt
& Whitney PW1000G engine, which has around 5,000 embedded sensors. These devices generate up to 10GB of data per second when in operation, meaning that a lone twin-engine aircraft with an average flight time of 12 hours could produce up to 844TB of data. The new generation of GEnx engines produces five to 10 TB of data per day, and data analytics supported by IoT are driving significant improvements. Rolls Royce has not published such data, but they are likely in the same range. Not only does this assist with quicker and better fault detection, it provides design engineers with information by which to upgrade their next set of products.
Only a small fraction of this data is communicated while in flight for cost and bandwidth reasons. The remainder is sent once an aircraft lands or when it is being serviced. While engines are leading the charge and embracing the IoT and data generation, avionics systems are also coming along for the ride. Traditional avionics systems transfer data up to a maximum of 12.5 KB/s whereas Boeing 787s and A350s are using deterministic Ethernet-based, next-generation aircraft data networks (AFDX, based upon ARINC 664).
This makes it quicker and easier to transmit information within the aircraft and via avionics systems to maintenance systems on the ground via comm links. Undoubtedly, avionics OEMs will take advantage of higher bandwidth communications to provide more status and lifecycle data over time. Many avionics are evolving to take advantage of the higher- bandwidth busses and this will have an effect on maintenance support personnel, due to the need to better understand networking protocols and issues.
A clear difference between consumer-oriented IoT devices and aircraft IoT devices is the value of the data. Most industrial and business- focused IoT implementations will need to meet clear business cases prior to any launch, and be able to demonstrate their need clearly. Not all data captured is equal — but in the case of aircraft engines, a combination of IoT and Big Data has also helped engine OEMs with their aftermarket support contracts pricing, and has financially benefited airlines/ operators as well.
IOT IS DRIVING PREDICTIVE AND
PRESCRIPTIVE MAINTENANCE As an example of how far industry has come forward, the Boeing 767 had approximately 9,000 detectable faults. Due to a vast increase in the number of on-board sensors on a Boeing 787, it can detect approximately 45,000 faults, five times as many as the rate 30 years ago. Newer aircraft from Airbus and Boeing will have up to thousands of sensors embedded in their wings alone. This is driving changes to how maintenance is approached. Aviation has been moving from condition-based aircraft maintenance to predictive maintenance. This is an example of ‘big data’ generated by connected devices and sensors
supporting the capability of software applications to identify pertinent data patterns that provide maintenance staff with direction on what needs to be handled to avoid aircraft downtime. Obtaining needed spare parts and optimizing work schedules will provide benefits to airlines/ operators, and this is where predictive maintenance will shine. Such data also supports the ability to better exchange data amongst operators, OEMs, regulators and MROs, once all such parties have upgraded their data management capabilities. This is one of challenges for the industry — how to upgrade the IT capabilities of everyone to share, exchange and secure such information better. New strong industry standards will need to emerge on how this data is structured, and how we authenticate the source of the information and protect its integrity while it is shared. Some of these efforts are already underway at IATA and the ATA/A4A. One notable example of how
predictive analytics extends beyond just analyzing engine data relates to GE. It has an advanced operational management capability for an aircraft, where it provides the data concentration and network, advanced power management and health management systems. It provides this for the Gulfstream G500/G600 business jets (powered by Pratt & Whitney PurePower PW814GA and PW9815GA engines), so its products work with other vendors engines as well. It is also providing a similar capability on the G650, which is powered by Rolls-Royce BR725 engines.
The follow-on to predictive maintenance is prescriptive maintenance. Being able to perform what-if scenarios on potential events identified in data patterns from IoT sensors will provide aircraft operators with an even greater
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