Objectives
To detect failure before they appear is a big challenge for any kind of complex systems. From modern car full of automation (sensors, actuators, control/command strategies) to more-electric airplanes, from industrial power plant to robotics applications, methods are needed to inform that a failure or default as appeared, appears or will appear. That course will focus on automatic detection methods based on model-based approaches or artificial intelligence approaches.
Palabras clave
Diagnosis, health monitoring, identification, pattern recognition,FMECA
Programme
Context Fonctional approches like FMECA (Failure Modes, Effects and Criticality Analysis) Reliability Diagnosis approaches:
- model-based
- identification
- error detection -artificial intelligence
- pattern recognition
- clustering
- decision rules Perspectives
Learning Outcomes
- To realise challenges and difficulties associated with health monitoring
- To be able to applied pattern recognition techniques
- To be able to properly identify mathematical model for diagnosis purposes
- To be able to select parameters identification methods
Assesment
Final mark = 50% Knowledge + 50% Know-how Knowledge = final exam Know-how = average mark issued from 3 reports from BE