Data-driven maintenance priority recommendations for civil aircraft engine fleets using reliability-based bivariate cluster analysis

Zhou, H. , Parlikad, A. K. and Brintrup, A. (2023) Data-driven maintenance priority recommendations for civil aircraft engine fleets using reliability-based bivariate cluster analysis. Quality Engineering, 35(4), pp. 584-599. (doi: 10.1080/08982112.2022.2163179)

Abstract

The modern civil aircraft engine is a type of highly complex engineering system in design, manufacturing, and life-cycle management. They are constantly operated under extreme and critical conditions, and yet, high reliability and safety are top priorities in the civil aviation industry. To ensure top performance and efficiency in operations, engines follow a modular design. This article intends to apply the data-driven cluster analysis to real-life operation data for aircraft engine fleets, which provides a module maintenance priority recommendation solution to increase the efficiency of operations and best use of the engine values.

• Item Type: Articles
• Additional Information: This research was funded by the Aerospace Technology Institute and Innovate UK, the UK’s innovation funding agency, through the “Digitally Optimised Through-Life Engineering Services” project (113174).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhou, Dr Hang
• Authors: Zhou, H., Parlikad, A. K., and Brintrup, A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Quality Engineering
• Publisher: Taylor & Francis
• ISSN: 0898-2112
• ISSN (Online): 1532-4222
• Published Online: 02 February 2023
• Copyright Holders: Copyright © 2023 The Author(s)
• First Published: First published in Quality Engineering 35(4):584-599
• Publisher Policy: Reproduced under a Creative Commons license