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

被引：3
|
作者：
Zhou, Hang [1 ,2 ]
Parlikad, Ajith Kumar [2 ]
Brintrup, Alexandra [2 ]
机构：
[1] Univ Glasgow, James Watt Sch Engn, Glasgow, Scotland
[2] Univ Cambridge, Inst Mfg, Dept Engn, Cambridge, England
来源：
QUALITY ENGINEERING | 2023年 / 35卷 / 04期
基金：
“创新英国”项目;
关键词：
aerospace engineering; civil aviation; clustering algorithms; fuzzy C-means; Gaussian mixture models; semantic analysis; PREDICTION; TIME; CLASSIFICATION; ALGORITHM;
D O I：
10.1080/08982112.2022.2163179
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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.
引用
收藏
页码：584 / 599
页数：16
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