An Architecture for Performing Real Time Integrated Health Monitoring of Aircraft Systems Using Avionics Big Data

Aircraft maintenance and its associated activities have many challenges. These include keeping aircraft downtime as minimum as possible and keeping an adequate inventory to replace a faulty component. Traditionally, aircraft maintenance was performed on the principle of condition based maintenance (CBM) i.e. performing maintenance due to degradation of system performance. Although this approach has served its purpose, the advent of new aircrafts like A380, A350 and B787 and exponential growth in the number of aircrafts flying today calls for renewed design approach towards aircraft maintenance. There is a growing industry requirement for changing the aircraft maintenance design from being reactive to proactive. Technological advancements have led to development from mechanical aircraft system to electrical aircraft system. Health checking of an aircraft has evolved from inspection to system health monitoring. There are now more sensors, more systems and more exchange of data among systems and ground stations. These advancements are supporting the industry needs for proactive maintenance approach, but these advancements have been in silos and have grown over time catering to requirements one by one at system and subsystem level leading to inconsistencies, inefficiencies and ineffectiveness. Hence a new architecture which takes a holistic view of complete aircraft as a system for performing real time aircraft health monitoring is required. The advent of sensor technologies, intelligent aircraft systems, avionics communication networks, high performance computation clusters and frameworks for big data analytics hold the key for designing real time and integrated health monitoring of aircraft systems. In this paper an attempt has been made to define sources for “on the fly” health monitoring data generation, an architectural framework for usage of ISO-13374 standard, OSA-CBM supplied UML diagrams and ARINC 664 standard for transmission of health monitoring data and perform just in time health assessment by offline and online prognostic modules using “on the fly” health monitoring data and Hadoop framework.