Phelma Formation 2022

Energy Monitoring and Diagnostics - 5PMSEMD0

  • Number of hours

    • Lectures 4.0
    • Projects 14.0
    • 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 and case studies
Contact Pierre GRANJON, Cornel IOANA

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

Semester 9 - The exam is given in english only 

technical reports (labworks and project)



En présentiel :
N1 = 50% CC + 25% Rapport + 25% Oral
N2 = 50% CC + 25% Rapport + 25% Oral

En distanciel :
N1 = 50% CC + 25% Rapport + 25% Oral
N2 = 50% CC + 25% Rapport + 25% Oral

Additional Information

Semester 9 - This course is given in english only EN

Course list
Curriculum->Engineering degree->Semester 9
Curriculum->Double-Diploma Engineer/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