Authors:
Qiaochu Li
1
;
Carine Jauberthie
2
;
Lilianne Denis-Vidal
1
and
Zohra Cherfi
1
Affiliations:
1
Sorbonne University and Université de Technologie de Compiègne, France
;
2
France Université de Toulouse, France
Keyword(s):
Diagnosis, Parameter Estimation, Nonlinear Systems, Interval Analysis.
Related
Ontology
Subjects/Areas/Topics:
Control and Supervision Systems
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Nonlinear Signals and Systems
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Systems Modeling and Simulation
Abstract:
Through parameter estimation schemes, one could be able to detect, localize and identify the occurring fault
via simple computation. Yet, certain faults may not be discovered even be mistaken in a normal condition
with unknown noises by trend checking or state monitoring. A more informative way when a correct model
is present to analyses the data via parameter estimation. In this paper, we propose by using interval analysis
a diagnosis scheme, from which we can extract the guaranteed diagnostic results to inform the supervisor so
that appropriate actions could be taken. Sending them the results in a guaranteed way to tell the diagnostician
which kind of fault exist is firstly taken care in diagnosis context. Our original fault detection and localization
procedure has been firstly proposed in an interval analysis context for the constant fault in parameters.
Moreover, another new technique in parameter estimation is the distance check, which speed up the estimation
procedure. Some dra
wbacks have been discussed in the end.
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