ATP’s Machine-Based Learning Solves Aircraft Data Problem

ATP CaseBank launched a new machine-based learning application, designed to help improve the accuracy of documenting Air Transport Association (ATA) codes. The new feature is the latest in the continuing evolvement of the company’s ChronicX software suite. ATP Casebank says ChronicX is used by more than 25 percent of the world’s commercial airline fleet to detect recurring or chronic issues on aircraft.

The company says that it is common for a medium-sized airline fleet producing 1,000 new records each day, to have 40 percent of defects not being flagged correctly to maintenance control due to incorrect ATA codes. The company says their new ChronicX ATA recoding feature can automatically predict the right four-digit ATA code for a defect based on its description, regardless of how it has been entered or reported.

In addition to providing four-digit accuracy at a 90 percent level, the new recoding application continuously learns from user feedback. This allows prediction accuracy to increase exponentially with continued usage. Airline teams can recode all their defects with reduced effort and in a fraction of the time it would take with other systems and processes, ChronicX contends. The data they are working with is also more reliable, which can contribute to better informed decisions.

“The airline industry has struggled for years with the accuracy of the ATA codes being applied to maintenance issues and its impact on the data they rely on to ensure the safety of their aircraft,” says James Geneau, chief marketing officer at ATP CaseBank. “By working closely with our global airline customers, our product team identified this as an opportunity where our in-house machine-learning experts could develop a solution for the industry. Maintenance technicians are extremely busy and focused on quickly getting planes fixed and moving,” adds Geneau. “This new feature allows them to stay focused on the job at hand while maintenance control can rely on technology to ensure a higher degree of accuracy in the overall data needed to do their job.”

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