5PMSEMD0 : Energy Monitoring and Diagnostics - WPMTDEM0

1. introduction to system monitoring and diagnosis (lecture)
2. sequential change detection in signals (lecture/labworks)
   • problem statement
   • CuSum type algorithms
   • GLR type algorithms
3. projects of system monitoring and diagnosis (project)

• basics of data science (correlation, spectral analysis, filtering, ...)
• basics of estimation theory (maximum likelihood estimator)
• basics of detection theory (binary hypothesis testing)

Session normale / First session
Evaluation rattrapable (ER) / ER assessment : 1 project / 1 project

Si situation 100% distancielle / If distant learning mandatory:
Evaluation rattrapable (ER) / ER assessment : 1 projet / 1 project

Moyenne de l'UE / Course Unit assessment = ER 100%
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Session 1 :
si cours en présence : rapport de projet (100%)
si cours à distance : rapport de projet (100%)

Session 2 :
si présentiel possible : rapport de projet (100%)
si distanciel imposé : rapport de projet (100%)

Detection of Abrupt Changes - Theory and Application. Michèle Basseville, Igor Nikiforov. Prentice Hall - http://people.irisa.fr/Michele.Basseville/kniga/, 1993.

Statistical inference for engineers and data scientists. Pierre Moulin, Venugopal V. Veeravalli. Cambridge University Press, 2019.

The CuSum algorithm - A small review. Pierre Granjon. Technical report - https://hal.archives-ouvertes.fr/hal-00914697, 2013