Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities

Stanton, Izaak; Munir, Kamran; Ikram, Ahsan; El-Bakry, Murad

Abstract

The increase in available data from sensors embedded in industrial equipment has led to a recent rise in the use of industrial predictive maintenance. In the aircraft industry, predictive maintenance has become an essential tool for optimizing maintenance schedules, reducing aircraft downtime, and identifying unexpected faults. Despite this, there is currently no comprehensive survey of predictive maintenance applications and techniques solely devoted to the aircraft manufacturing industry. This article is an in-depth state-of-the-art systematic literature review of the different data types, applications, projects, and opportunities for predictive maintenance in this industry. The goal of this review is to identify, and highlight the challenges and opportunities for future research in this field. This review found that the current focus of research is too biased towards aircraft engines due to a lack of publicly available data sets, and that greater automation is an important step to optimize aircraft maintenance to its fullpotential.

• Journal Article Type: Article
• Acceptance Date: Nov 16, 2022
• Online Publication Date: Dec 25, 2022
• Publication Date: Mar 1, 2023
• Deposit Date: Nov 18, 2022
• Publicly Available Date: Dec 26, 2023
• Journal: Systems Engineering
• Print ISSN: 1098-1241
• Electronic ISSN: 1520-6858
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 2
• Pages: 216-237
• DOI: https://doi.org/10.1002/sys.21651
• Keywords: Aircraft maintenance; Predictive maintenance; Machine learning; Deep learning; Big Data Analytics
• Public URL: https://uwe-repository.worktribe.com/output/10146762
• Publisher URL: https://incose.onlinelibrary.wiley.com/doi/10.1002/sys.21651