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Pentagon taps Silicon Valley firm for aircraft predictive maintenance

A Silicon Valley-based firm will deliver software to predict maintenance activity for US Air Force aircraft, starting with the Boeing E-3 Sentry and Lockheed Martin F-16.

The Defense Innovation Unit Experimental (DIUx) selected the software firm C3 IoT for a multi-year contract aimed at leveraging the technology industry's advances in artificial intelligence and machine learning for aircraft maintenance, the company says.

C3 IOT was founded by Tom Siebel, a software entrepreneur whose first company, Siebel Systems, was acquired by Oracle in 2005 for $5.8 billion. Siebel is now chief executive of C3 IoT, which describes itself as an Internet of Things platform and applications company. The company's cloud-based computing architecture gained the notice of DIUx, an acquisition arm of the Department of Defense searching for potentially disruptive technologies in Silicon Valley for the aerospace and defence industries.

The first two aircraft covered under the new contract will be the E-3 Sentry airborne warning and control system (AWACS) and F-16 fleets, with delivery of the software scheduled in six months.

The new software platform should allow the Pentagon to collect large amounts of data, including sensor reports and maintenance logs, into Amazon Web Services’ GovCloud, which hosts sensitive but unclassified information, C3 IoT says. Artificial intelligence and machine learning will be used to predict imminent failures on components and across entire systems.

Smaller, more nimble concepts from Silicon Valley, such as C3’s predictive software, could strike a blow to defense industrial base giants.

Last spring, Boeing announced the company would embark on an ambitious plan to expand into the aerospace services market, with its new Global Services unit. With Lockheed Martin holding a firm lock on current fighter production in the US with the F-35 Lightning II, Boeing Global Services could capture some valuable after market work. Like C3 IoT, the BGS suite of analytic tools promise to help predict component or part failures before they happen.

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