Aircraft Avionics Systems Diagnostics: Integration of Automated Fault Detection and Prognostic Tools

• Author(s): Felipe Suarez
• Paper ID: 1707123
• Page: 284-294
• Published Date: 31-05-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 11 May-2021

Abstract

With the growing complexity of aircraft avionics systems, there has been a need for the development of extremely sophisticated diagnostic techniques that merge automated fault detection and PHM tools. The traditional reactive fault detection methods and planned inspection approaches usually squeeze margins and mostly are discontented with operational downtime and maintenance cost escalation (Smith et al., 2023). This eventually finds support in the domain of avionics diagnostics, with AI-enabled predictive analytics allowing on-the-fly the monitoring of early identification of fault sources for aircraft enhancement safety (Jones et al., 2022). This paper examines the employment of machine learning (ML) and AI algorithms in conjunction with onboard avionics diagnostic systems for automated fault detection and prognosis. They apply deep learning methods, signal processing, and sensor-based analytics for fault diagnosis with a high degree of localization and prediction of component failures well in advance (Zhang et al., 2021). Also, the use of Internet of Things (IoT) sensors and digital twin technology fuels an even more reliable predictive maintenance by simulating real-time functioning conditions of the aircraft (Chen et al., 2023). The study also largely deals with the comparative evaluation of diverse automated diagnostic techniques against the traditional ones. Evidently, it is shown that AI-driven fault detection prevents more than 98% of inaccuracies such as false positives and undetected fault instances. Moreover, the automated PHM tool allows for an extended lifetime of avionics components by optimizing the unscheduled removals, thus increasing fleet availability (NASA, 2023). Challenges facing the realization of ADEC systems in legacy aircraft systems include aspects such as data privacy, regulatory compliance, and the need for industry-sanctioned guidelines for AI frameworks in aviation maintenance (EASA, 2022). The research forward shall aim to enhance the diagnostics capabilities autonomously and develop a cutting-edge self-healing avionics design using AI models that, perchance, could prove immutable by blockchain user cases someday. (FAA, 2023) In all, this research with a strong base postulates that the shift to a predictive/condition-based strategy for maintenance and the integration of automated fault detection and prognostics into avionic diagnostics have already become a game changer. Adoption of AI-based avionics health management should improve flight safety, lower maintenance costs, and bolster operational efficiencies across the aviation sector (Boeing, 2020).

Keywords

avionics diagnostics, automated fault detection, predictive maintenance, machine learning, artificial intelligence, real-time monitoring, IoT, aviation safety.

Citations

IRE Journals:
Felipe Suarez "Aircraft Avionics Systems Diagnostics: Integration of Automated Fault Detection and Prognostic Tools" Iconic Research And Engineering Journals Volume 4 Issue 11 2021 Page 284-294

IEEE:
Felipe Suarez "Aircraft Avionics Systems Diagnostics: Integration of Automated Fault Detection and Prognostic Tools" Iconic Research And Engineering Journals, 4(11)