Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Abstract

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.

Novel Prognostic Methodology of Bootstrap Forest and Hyperbolic Tangent Boosted Neural Network for Aircraft System

“…• A method was presented by [5] that uses data fusion with stage division to forecast the RUL of a system. This method, called an ensemble RUL prediction method, has been shown to achieve considerable gains in accuracy.…”

“…The most widely favoured options are enumerated as follows. The physics-based methodology necessitates a precise physical representation of system behaviour, encompassing both normal and defective states [5]. By comparing the data obtained from sensors with the model's predictions, one can infer the health of the system [6].…”

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

“…Prognostics and health management is crucial for the safety and reliability of aircraft systems, focusing on predicting subsystems' remaining useful life and mitigating future failures through predictive modeling and real-time data analysis. The paper [10] reviews the current research on PHM in aviation, highlighting key algorithms, applications, challenges, and future research directions.…”