Condition-Based Maintenance in Aviation: Challenges and Opportunities

被引:11
作者
Verhagen, Wim J. C. [1 ]
Santos, Bruno F. [2 ]
Freeman, Floris [3 ]
van Kessel, Paul [3 ]
Zarouchas, Dimitrios [4 ]
Loutas, Theodoros [5 ]
Yeun, Richard C. K. [1 ]
Heiets, Iryna [1 ]
机构
[1] RMIT Univ, Sch Engn, Aerosp Engn & Aviat, Melbourne, Vic 3000, Australia
[2] Delft Univ Technol, Fac Aerosp Engn, Air Transport & Operat, Kluyverweg 1, NL-2629 HS Delft, Netherlands
[3] Amsterdam Airport Schiphol, Koninklijke Luchtvaart Maatschappij KLM Engn & Mai, NL-1117 ZL Amsterdam, Netherlands
[4] Delft Univ Technol, Fac Aerosp Engn, Ctr Excellence Artificial Intelligence Struct Prog, Kluyverweg 1, NL-2629 HS Delft, Netherlands
[5] Univ Patras, Dept Mech & Aeronaut Engn, Univ Campus, Patras 26504, Greece
关键词
condition-based maintenance; integrated vehicle health management; structural health monitoring; prognostics and health management; maintenance planning; PREDICTIVE MAINTENANCE; PROGNOSTICS; FLEET; SYSTEMS;
D O I
10.3390/aerospace10090762
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Condition-Based Maintenance (CBM) is a policy that uses information about the health condition of systems and structures to identify optimal maintenance interventions over time, increasing the efficiency of maintenance operations. Despite CBM being a well-established concept in academic research, the practical uptake in aviation needs to catch up to expectations. This research aims to identify challenges, limitations, solution directions, and policy implications related to adopting CBM in aviation. We use a generalizable and holistic assessment framework to achieve this aim, following a process-oriented view of CBM development as an aircraft lifecycle management policy. Based on various inputs from industry and academia, we identified several major sets of challenges and suggested three primary solution categories. These address data quantity and quality, CBM implementation, and the integration of CBM with future technologies, highlighting future research and practice directions.
引用
收藏
页数:23
相关论文
共 46 条
[1]  
Airlines for America, 2018, MSG-3: Operator/Manufacturer Scheduled Maintenance Development, VOLUME 1-FIXED WING AIRCRAFT
[2]   Review:: Knowledge management and knowledge management systems:: Conceptual foundations and research issues [J].
Alavi, M ;
Leidner, DE .
MIS QUARTERLY, 2001, 25 (01) :107-136
[3]   Prediction of Aircraft Failure Times Using Artificial Neural Networks and Genetic Algorithms [J].
Altay, Ayca ;
Ozkan, Omer ;
Kayakutlu, Gulgun .
JOURNAL OF AIRCRAFT, 2014, 51 (01) :47-53
[4]  
Alter S, 2013, J ASSOC INF SYST, V14, P72
[5]  
[Anonymous], IATA Airline Maintenance Cost Executive Commentary (FY2020 Data)
[6]  
[Anonymous], 2011, Flightpath 2050 Europe's vision of aviation: maintaining global leadership and serving society's needs, DOI DOI 10.2777/50266
[7]  
[Anonymous], 2017, MaintenanceMaintenance Terminology
[8]  
Atamuradov V, 2017, International Journal of Prognostics and Health Management, V8, P1, DOI [DOI 10.36001/IJPHM.2017.V8I3.2667, 10.36001/ijphm.2017.v8i3.2667]
[9]   Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI [J].
Barredo Arrieta, Alejandro ;
Diaz-Rodriguez, Natalia ;
Del Ser, Javier ;
Bennetot, Adrien ;
Tabik, Siham ;
Barbado, Alberto ;
Garcia, Salvador ;
Gil-Lopez, Sergio ;
Molina, Daniel ;
Benjamins, Richard ;
Chatila, Raja ;
Herrera, Francisco .
INFORMATION FUSION, 2020, 58 :82-115
[10]  
Berger J., 2023, IATA MAINT COST C