Built-In Prognostics and Health Management (BI-PHM): A novel concept for Prognostics and Health Management implementations in Aviation

In the aviation industry, avoiding Aircraft on Ground (AOG) events is imperative for ensuring operational continuity, safety, and reduced lifecycle cost, and Prognostics and Health Management (PHM) has emerged as a key approach to this end by combining sensor measurements with mathematical models to comprehensively manage aircraft health. However, the effective deployment of PHM is hindered by several persistent obstacles: limited access to high-quality operational data, sparse failure events across fleets, the need for long time-series records, and a knowledge gap between operators, who often lack detailed system insight, and Original Equipment Manufacturers (OEMs), who are reluctant to share proprietary system knowledge for Intellectual Property (IP) reasons. To address these challenges, this paper proposes Built-In Prognostics and Health Management (BI-PHM), a novel concept in which predictive functionality is embedded directly into system components, extending the ubiquitous Built-In Test (BIT) functionality. Under BI-PHM, system OEMs develop and integrate the prognostic functionality within their own components, leveraging their system knowledge while sharing only vital, explainable health indicators with operators and maintenance organisations, thereby safeguarding their IP. The concept is structured around two complementary functionalities: onboard health management and offboard algorithm development and refinement. By restructuring the roles and data flows among stakeholders, BI-PHM offers a more effective path to PHM implementation, with the potential to reduce AOG incidents, lower lifecycle costs, and improve maintenance planning for the benefit of all parties in the aircraft maintenance ecosystem.

Keywords: Prognostics and Health Management, Maintenance Technology, Predictive Maintenance, Original Equipment Manufacturer, Edge Computing.