Improvements to jet engines have greatly improved aircraft range, efficiency and safety. The traditional drivers for this improvement have been predominately related to design: optimised components, new materials and innovative manufacturing techniques. Machine learning offers jet engine manufactures like Rolls-Royce a new avenue to improve cost and engine performance by optimising the maintenance of engines in service.
Rolls-Royce sells a majority of engines on service contracts instead of an upfront purchase price. Under these contracts, the operator pays Rolls-Royce per hour of flying time and as a function of the engine’s efficiency. Therefore, operators and Rolls-Royces’ incentives are aligned to keep engines on wing as long as possible and at maximum efficiency. Engine efficiency degrades with age and component wear though, so the challenge is deciding when to take engines off-wing to perform maintenance, and which components to replace or repair. With 13,000 engines in service, small improvements to engine maintenance schedules would significantly boost earnings.
Currently, engineers monitor the level of wear and engine performance with regular inspections and real-time data. Judgements on when to take engines off-wing for maintenance are based on engineering models and previous experience. Although these methods have improved over time, machine learning offers a step change. This is because it can use a wealth of new data being made available from real-time sensors and 3D scanning technologies. In addition, newer engine models can use algorithms developed from older models, shortcutting the lengthy period to build service maintenance data.
To develop advanced machine learning algorithms requires Rolls-Royce to build appropriate datasets, and organisational capability.
The current CEO, Warren East, is looking to bolster Rolls-Royce’s digital capabilities through internal and external changes. The digital organisation has been strengthened as part of an internal restructuring. A partnership with Microsoft in 2016 is helping to develop more advanced engine analytical tools. Numerous programs have been launched in tandem with partner research universities to build machine learning algorithms.
To improve engine monitoring, newer engine models are equipped with more real-time sensors that gather more data and relay it directly in real-time to Rolls-Royce. In addition, data from older engine models is being gathered and processed to further boost the engine performance datasets. This has been a challenge for older engines with more service history but data that is stored in legacy systems and structures. Training data is being further bolstered by Rolls-Royce’s growing 3D printing capabilities. Many types of damaged components can be manufactured quickly and cheaply. Specific datasets can then be generated by testing the components on rigs.
The EJ200 (powering the UK’s Eurofighter Typhoon) is proving to be a “goldilocks” engine to experiment with maintenance schedules tailored to individual engines. The engine first flew in 2003, and is equipped with cutting edge data gathering equipment that has generated a significant amount of in-service history data since. Comparisons in individual engine performance to historical data allows engineers to adjust maintenance to meet what the engine needs. To improve their judgement, engineers are working to marry the engine performance data with inspection data gathered from components. New 3D scanning inspection techniques are being used to build accurate dimension information, at low cost, on complex components (like aerofoils) that can help train machine learning algorithms to more accurately determine which components need replacing. These efforts are laying the groundwork to develop and roll out machine learning generated maintenance schedules.
There is a risk that these efforts are being pursued adjacent to other parts of the organisation and so wont maximise the benefits. System and component design teams are still composed of traditional aerospace engineers in silos separated from the digital capability teams, hindering the exchange of ideas. This is important as analysis from the machine learning algorithms could be used to drive the design of components and systems to maximise their lifetime value. Communication and the exchange of ideas between these teams should be improved through community of practices, joint teams and colocation where possible.
Another opportunity is for algorithms to provide feedback to the operator. Decisions on flight routings can impact engine degradation and performance. By combining flight data with engine performance and maintenance data, machine learning algorithms could be trained to analyse proposed flights. Operators would then be able to optimise flight routes to reduce fuel burn and engine degradation.
Rolls-Royce has succeeded over the past 100 years by building cutting edge hardware. Developing machine learning will require new skills. Can the organisation develop the digital capability to fully capitalise on this mega-trend? Should Rolls-Royce partner with external organisations?
 Rolls-Royce, “Annual report 2017”, https://www.rolls-royce.com/~/media/Files/R/Rolls-Royce/documents/annual-report/2017/2017-full-annual-report.pdf, accessed November 2018
 Rolls-Royce, “Rolls-Royce takes TotalCare digital with Microsoft and Singapore Airlines”, https://www.rolls-royce.com/media/press-releases/2016/11-07-2016-rr-takes-totalcare-digital-with-microsoft-and-singapore-airlines.aspx, accessed November 2018
 Rolls-Royce plc website, “EJ200”, https://www.rolls-royce.com/products-and-services/defence-aerospace/combat-jets/ej200.aspx#/, accessed November 2018
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