Manutenção de Condição e Predição

The lectures are mostly expository, with presentation and discussion of the topics of the program.
They are also used for presentation and discussion of works and practical examples of application of technologies, including guest lectures.

In practical classes, exercises, practical work and presentations are held.

1. Knowledge of tools and techniques for data collection
2. Knowledge of techniques and tools for failure analysis
3. Knowledge of techniques and tools for intelligent forecasting and prognostication
4. Use of tools and methods to support decision-making

1. Data Collection Strategies, Tools, and Technologies
1.1 Types of Sensors
1.2 Types of Variables
2. Fault Analysis Techniques and Tools
2.1 Electromechanical Systems
2.2 Data Analysis Tools
3. Prediction and Prognostics in Digital Signals
3.1 Time Series Concepts in Condition Maintenance
3.2 Prediction Using Recurrent Computational Models
4. Decision Support Systems
4.1 Knowledge-Based Systems
4.2 Digital Twins and Metaverse
4.3 Modeling and Simulation Tools
4.4 Generative AI Models in Maintenance

• - Exam - 60.0%
• - Practical work - 40.0%

• - Practical work - 40.0%
• - Attendance and Participation - 10.0%
• - Frequency - 50.0%

NAO