Data mining navy flight and maintenance data to affect repair

A large portion of Naval aircraft maintenance is driven by avionics-related deficiencies. Military avionics systems rely heavily on built-in test (BIT) to troubleshoot discrepancies during unscheduled maintenance. One study found that analyzing BIT codes for trends (both at the aircraft level and at the squadron level) and scheduling maintenance accordingly, increased aircraft operational availability (A
_o
) by twenty-two percent within a single squadron [1]. The study focused on F/A-18C aircraft over a forty-four month period. While this is a substantial increase, it takes into account only organizational level (O-level) BIT data. It does not include all available information, such as historical maintenance data, operating environment, and past repair history. The Integrated Diagnostics and Automated Test Systems (IDATS) team at Naval Air Systems Command (NAVAIR) Lakehurst is investigating the use of data mining to mitigate ambiguity within Naval avionics maintenance, with the intent of reducing costly and inefficient maintenance practices [2]. This includes building models from both aircraft historical maintenance data and BIT codes recorded to an aircraft's memory unit (MU) during a flight in order to identify trends in the data that would not be obvious or trivial to a maintainer. The IDATS team has utilized a commercial off-the-shelf (COTS) data mining software package called ThinkAnalytics in combination with custom software tools to find meaningful trends within the F/A-18 BIT and maintenance datasets. ThinkAnalytics is a real-time enterprise data mining tool that provided the necessary data mining functionality. Trends identified by the software are currently being validated by the user and system Subject Matter Experts (SMEs) to ensure that they are accurate, novel and/or non-trivial. Through the use of data mining, in combination with knowledge about how the system operates and communicates with other systems, BIT can be augmented to improve maintenance efficiency at all levels of maintenance.