Titulo Estágio
ReMAP Adaptive Maintenance Management Decision Support Tool
Áreas de especialidade
Sistemas de Informação
Engenharia de Software
Local do Estágio
Coimbra
Enquadramento
Aircraft maintenance is currently carried out following one of two strategies: reactive, (also known as ‘fix when it fails’) and preventive (also known as pre-scheduled or interval-maintenance). The fusion of the emerging concepts of IoT, Big Data and Machine Learning have introduced a new promising alternative based on Integrated Fleet Health Monitoring.
Integrated Fleet Health Monitoring will enable a strategy to only repair aircraft parts that are actually damaged or to replace parts that are close to failure. A reliable network of sensors will cover most aircraft systems and primary structural elements, monitoring their health on a permanent basis. Efficient Machine Learning data analytics algorithms and physics models will process the terabytes of data generated per day by these sensors, providing operators with situational awareness and status of the entire fleet. On-board, lean algorithms will be able to diagnose the existence of systems’ faults that can be immediately communicated to the maintenance control centre (i.e., using an edge computing approach). On the ground, powerful algorithms will fuse the coming data generated by the aircraft with historical data to precisely diagnose the existence, location, and severity of structural damages or systems’ faults. Other algorithms will use the same type of data to predict the remaining useful life of systems and structures, anticipating the need for maintenance.
Objetivo
Main Goal will be to study the maintenance context and propose an initial design for the interface to satisfy the foreseeable operative conditions under the new tools. This will imply availability to get involved with the EU ReMAP project team, elicit relevant information and propose interface designs for the condition-based maintenance tool and, make prototypes to be studied in interaction with relevant machine learning components under study. This will be an excellent opportunity to work on the novel frontier of designing ways for human interfacing with AI. The result should be a stable version of an interface prototype proposal and user studies.
Plano de Trabalhos - Semestre 1
A1. (M1-M3) State of the art research - research and document the relevant publications and software, study interaction design techniques for the study of the problem at hand;
Overview of project context and importance of Condition Based Maintenance for Fleet Health Management; Propose initial predictive model workflow including the ML/AI techniques being developed by partners and prepare first case study;
A2. (M2 - M3) Detailed proposal - detailed characterization of the problem to be solved stating specific goals and requirements, assumptions and milestones for the rest of the project;
A3. (M4 - M5) Initial Design - porting the current prototype, performing technology tests, identification of quality attributes, initial architectural design for the proposed solution, including user interface model
Plano de Trabalhos - Semestre 2
A4. (M6 - M10) Prototyping - implementation and testing of a working prototype, tuning or corrections based on evaluation results
;
A5. (M9) Evaluation - definition of evaluation criteria and method, performance or evaluation and analysis of results
A6. (M6 - M10) Statement of Learning - production of a research paper and dissertation with process report and reflection on knowledge production
Condições
This work will be carried out at CISUC (Information Systems Group), where there will be a regular supervision.
Good knowledge and research interest in the area of Human-Computer Interaction is a requirement.
A priori familiarity with machine learning and data mining algorithms and software tools is preferable but not essencial.
Participating students will likely acquire valuable knowledge and experience with data science tools.
The candidate should have an interest in research settings and good English proficiency.
Observações
This proposal is supported by the funded H2020 project REMAP (Real-time Condition-based Maintenance for Adaptive Aircraft Maintenance Planning): https://www.cisuc.uc.pt/projects/show/257.
A 3-month scholarship of 745 euros per month is foreseen for this work, renewable for another 3 months.
Orientador
Licinio Roque
lir@dei.uc.pt 📩