Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept

被引:47
作者
Ferreiro, Susana [1 ]
Arnaiz, Aitor [1 ]
Sierra, Basilio [2 ]
Irigoien, Itziar [2 ]
机构
[1] Fdn TEKNIKER, Eibar, Gipuzcoa, Spain
[2] Univ Basque Country, San Sebastian, Gipuzcoa, Spain
关键词
Prognosis; Bayesian network; Aircraft maintenance; Prediction; Brake degradation; PHM system; Predictive maintenance; Operability; DECISION-SUPPORT; MAINTENANCE; SYSTEMS;
D O I
10.1016/j.eswa.2011.12.027
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The aeronautics industry is attempting to implement important changes to its maintenance strategy. The article presents a new framework for making final decision on aeroplane maintenance actions. It emphasizes on the use of prognostics within this global framework to replace corrective and Preventive Maintenance practise for a predictive maintenance to minimize the cost of the maintenance support and to increase aircraft/fleet operability. The main objective of the article is to show the Bayesian network model as a useful technique for prognosis. The specific use case for predicting brake wear on the plane is developed based on this technique. The network allows estimate brake wear from the aircraft operational plan. This model, together with other models to make predictions for various components of the aeroplane (that should be monitored) offers a forward-looking approach of the status of the plane, allowing later the evaluation of different operational plans based on operational risk assessment and economic cost of each one of them depending on the scheduled checks. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:6402 / 6418
页数:17
相关论文
共 50 条
[41]  
Larranaga P, 2005, MACH LEARN, V59, P211, DOI 10.1007/s10994-005-0468-1
[42]   On the use of Bayesian Networks to develop behaviours for mobile robots [J].
Lazkano, E. ;
Sierra, B. ;
Astigarraga, A. ;
Martinez-Otzeta, J. M. .
ROBOTICS AND AUTONOMOUS SYSTEMS, 2007, 55 (03) :253-265
[43]   Intelligent prognostics tools and e-maintenance [J].
Lee, Jay ;
Ni, Jun ;
Djurdjanovic, Dragan ;
Qiu, Hai ;
Liao, Haitao .
COMPUTERS IN INDUSTRY, 2006, 57 (06) :476-489
[44]  
Lung B, 2005, CIRP ANN-MANUF TECHN, V54, P5
[45]   Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system [J].
Muller, Alexandre ;
Suhner, Marie-Christine ;
Lung, Benoit .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2008, 93 (02) :234-253
[46]  
Przytula K. W., 2007, IEEE AEROSPACE C, P1, DOI [10.1109/AER0.2007.352872, DOI 10.1109/AER0.2007.352872]
[47]   Predicting survival in malignant skill melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches [J].
Sierra, B ;
Larranaga, P .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 1998, 14 (1-2) :215-230
[48]  
Vachtsevanos G. J., 2006, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, V456
[49]   Prognosis of machine health condition using neuro-fuzzy systems [J].
Wang, WQ ;
Golnaraghi, MF ;
Ismail, F .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2004, 18 (04) :813-831
[50]   Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes [J].
Weidl, G ;
Madsen, AL ;
Israelson, S .
COMPUTERS & CHEMICAL ENGINEERING, 2005, 29 (09) :1996-2009