A Qualitative Event-Based Approach to Continuous Systems Diagnosis

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discrete-event diagnosis methods are used extensively, they do not easily address parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel event-based approa...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on control systems technology 2009-07, Vol.17 (4), p.780-793
Main Authors: Daigle, M.J., Koutsoukos, X.D., Biswas, G.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Continuous time systems
Control systems
Control theory. Systems
Deviation
Diagnosis
Diagnostic systems
Discrete-event system (DES)
Dynamical systems
Dynamics
electrical power systems
Exact sciences and technology
Fault diagnosis
Faults
Industrial metrology. Testing
Mechanical engineering. Machine design
model-based diagnosis
Modelling and identification
Noise level
Noise levels
Power system dynamics
Power system faults
Power system modeling
Power system simulation
Space vehicles
Studies
System testing
Systems engineering and theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discrete-event diagnosis methods are used extensively, they do not easily address parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel event-based approach for diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. From a continuous model of the system, we systematically derive dynamic fault signatures expressed as event-based fault models, which are used, in turn, for designing an event-based diagnoser of the system and determining system diagnosability. The proposed approach is applied to a subset of the Advanced Diagnostics and Prognostics Testbed, which is representative of a spacecraft's electrical power system. We present experimental results from the actual testbed, as well as detailed simulation experiments that examine the performance of our diagnosis algorithms under different fault magnitudes and noise levels.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2008.2011648