Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities

被引:18
作者
Stanton, Izaak [1 ,3 ]
Munir, Kamran [1 ]
Ikram, Ahsan [1 ]
El-Bakry, Murad [2 ]
机构
[1] Univ West England UWE, Comp Sci Res Ctr CSRC, Dept Comp Sci & Creat Technol, Bristol, England
[2] Airbus Operat Ltd, Pegasus House, Bristol, England
[3] Univ West England UWE, Comp Sci Res Ctr CSRC, Dept Comp Sci & Creat Technol, Bristol, England
关键词
aircraft maintenance; Big Data analytics; deep learning; machine learning; predictive maintenance; USEFUL LIFE PREDICTION; FAILURE;
D O I
10.1002/sys.21651
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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 full potential.
引用
收藏
页码:216 / 237
页数:22
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