Course Overview

There ar only tree main approaches. The observer-based approach, the parity-space approach, and parameter identification-based methods. In order to optimize FDI indications, the following two step are developped :
The first step is to design a filter based on a model of the plant to generate a vector known as the residual. The residual should ideally be zero (or zero mean) under no-fault conditions.
The second step is to make decisions on whether a fault has occurred. This step is usually done using statistical tools to test if the residual has significantly deviated from zero.

http://esisar.grenoble-inp.fr/en/academics/model-based-fault-diagnosis-for-linear-systems-5amac514

Learning Achievement

Competence

Fault detection and isolation (FDI) is a subfield of control engineering which involves monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Model-based techniques of fault detection and isolation use a model to investigate/analyze the occurrence of faults. The system model may be mathematical or knowledge-based. We focus our attention on mathematical models.

Course prerequisites

State space representation
Observer design
Identification H2
Algebre of matrice : Rank, ker, eigenvalue ...

Grading Philosophy

Final exam session 1, calculators authorized + 1 handwritten sheet A4 R/V, duration 1h30.

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Damien KOENIG

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

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