Titulo Estágio
Learning model for predictive aircraft maintenance
Áreas de especialidade
Sistemas Inteligentes
Local do Estágio
DEI-FCTUC
Enquadramento
This internship will take place in the context of the REMAP project. The challenge of the REMAP project is to use aircraft predictive diagnosis to define maintenance plans for a fleet.
Objetivo
In this internship the student should conceive and develop learning models capable of proposing/optimising maintenance plans for an aircraft fleeting predictive diagnosis of parts and systems. This activity includes the following actions:
- Dataset and data representation preparation
- Specification of the learning model
- Specification of the algorithm, fine-tuning of the model, test and validation.
- Deployment and test of the conceived architecture
- Integration of intelligent data processing modules into cloud services and validation of
models.
Plano de Trabalhos - Semestre 1
- Analyse the state of the art data and possible learning architectures (2 months);
- Define the learning architecture of the system (1 months);
- Start implementing the learning system (1 month)
- Write intermediate report (1 month);
Plano de Trabalhos - Semestre 2
- Implement the learning system (2 months);
- Test and evaluate performance (2 months);
- Write final report (1 month);
Condições
This work will be carried out in the Laboratory of Neural Networks (LARN) of CISUC, where there will be a regular supervision and feedback on the behalf of the supervisor and co-supervisor.
Familiarity with machine learning and data mining algorithms and software tools are essential. Participating students will acquire valuable knowledge and experience with model building and data science by mining massive datasets, which skills are currently in high demand for various technology employers due to the relevance to various applications.
Observações
Grant under ReMAP Project will be available during the second semester internship depending on the 1st semester candidate evaluation. A 3-month scholarship of 745 euros per month is foreseen for this work, renewable for another 3 months.
Logistics @Laboratory of Neural Networks (LARN)
DEI-FCTUC
Orientador
Bernardete Ribeiro, Alberto Cardoso, Joel Arrais, Catarina Silva
bribeiro@dei.uc.pt 📩