Data Pipeline Considerations for Aviation Maintenance

Abstract

In the aviation space, maintenance is the main driver in the push for Internet of Things (loT) device management systems, artificial intelligence (AI)/machine learning (ML) re-search, and cloud infrastructure. The potential for this approach to reduce downtime, maximize component lifetime, re-duce man-hours on diagnosis and repair, and optimize supply chains and scheduling has driven massive investments across the industry. And yet, the challenges in delivering on these promises with the available data and technology should also not be minimized. To reach its full potential, maintenance program implementers must understand what predictions can be derived from the available data, what maintenance actions may be driven by those predictions, and how the predictions should be presented to the appropriate decision makers in ground operations and the logistics chain. This report examines the current state of data within the aviation maintenance space, variations in component level coverage, and how that translates to the type, volume, and timeliness of data and computational infrastructure necessary to provide right time predictions and analytics to maintainers, supply chain managers, and operators. This report also addresses some of the specific challenges in the aviation space with respect to data availability, equipment variability, use variability, and maintenance action coding that can affect the ability of operators to derive value from a data science program.