Predictive Simulation Guides Fighter Jet Maintenance for RAF

Helping to keep the aircraft airborne and fighting were advanced predictive modeling techniques, deployed by a team of personnel from defense contractor and aircraft manufacturer BAE Systems, targeted on identifying likely maintenance needs.

Equipment maintenance is a growing area for predictive analytics, as companies such as IBM and GE use the ability to collect and analyze sensor data from machines (and parts of machines) to detect wear and predict when a key mechanical piece might fail. Apache Corp., an oil company, uses such algorithms to prevent the failure of essential underground and underwater pumping equipment to avoid spilling valuable crude.

Fighter jets are another example. But as well as relying on on-board sensors and instrumentation to flag maintenance needs, BAE Systems is also using Witness analytics and simulation software from Lanner. The team’s role is to model each Typhoon’s operational life, incorporating its maintenance history and the type and location of the missions it flies, and use this information to predict the associated consumption and re-supply requirements of the relevant spare parts—predicting maintenance requirements before on-board instrumentation signals a need for action.

Part of an initial five-year £450 million (about $700 million in today’s dollars) contract for maintenance outsourcing signed with Britain’s Ministry of Defence in 2009, the use of predictive modeling underpins an initiative to save the taxpayer £2 billion over the 25-year anticipated lifespan of the Typhoon contract.