A Systematic Literature Review of Predictive Maintenance for Defence Fixed-Wing Aircraft Sustainment and Operations

Abstract

In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available.

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

“…For more information, the author recommends reading, for example, [ 10 , 17 ]. On the other hand, a literature review on Maintenance 4.0 and the smart industry can be found among others in [ 15 , 36 , 42 ].…”

Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

“…Accurate estimation gives rise to several potential benefits, including lower maintenance costs and increased availability. However, while academic and industrial examples of successful PHM applications are on the rise, several challenges are currently present [2], including (1) a small amount of failure events, which complicates the development of data-driven PHM models in particular; (2) the use of sensors which are not specifically targeted to support PHM; (3) a lack of publicly available data to generate new and/or better-performing models; (4) a comparative lack of model validation on real-life datasets and across multiple components, with many available models developed for specific applications but not tested more broadly; and (5) consistent interpretation, explainability and reliability of PHM models and their output in a safety-oriented industry where mistakes may lead to major accidents.…”

Condition-based-maintenance for fleet management

“…A plethora of maintenance solutions have hence engendered to pitch in these scenarios, bolstered by the parallel growth in sensors technologies and Industry 4.0: starting from Opportunistic Maintenance (OM), passing through Condition-Based Maintenance (CBM) and even approaching predictive maintenance. If OM proposes an intelligent regrouping of maintenance activities, CBM and predictive maintenance [3] tries to plan maintenance actions according to the real component/subsystem health status. If the idea behind the CBM concept is, at least, quite straightforward, the implementation is anything but simple.…”