Phelma Formation 2022

5PMSEMD0 : Energy Monitoring and Diagnostics - WPMTDEM0

  • Number of hours

    • Lectures 4.0
    • Projects 0
    • Tutorials 0
    • Internship 0
    • Laboratory works 2.0
    • Written tests 0

    ECTS

    ECTS 2.0

Goal(s)

  • define the problem of system monitoring and diagnosis
  • complete the tools and methods necessary for this type of application
  • acquire autonomy through practical experiences
Contact Cornel IOANA, Pierre GRANJON, Ronald PHLYPO

Content(s)

  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)


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

Test

  • All documents authorized
  • Calculator authorized


Moyenne de l'UE / Course Unit assessment = ER 100%
--------------------------------------------------------------------------------
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%)

Additional Information

Course list
Curriculum->Double-Diploma Engineer/Master->Semester 9
Curriculum->Master->Semester 9

Bibliography

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