Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Bayesian approaches to failure prediction for disk drives
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The SACSO methodology for troubleshooting complex systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Intelligent prognostics tools and e-maintenance
Computers in Industry - Special issue: E-maintenance
On the use of Bayesian Networks to develop behaviours for mobile robots
Robotics and Autonomous Systems
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Enhancing ontology-based antipattern detection using Bayesian networks
Expert Systems with Applications: An International Journal
Embedded holonic fault diagnosis of complex transportation systems
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
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.