Wavelet based residual evaluation for fault detection and isolation

Fault detection and isolation (FDI) is an important issue for safe operation in industrial processes. To avoid false alarms, the FDI scheme must be robust enough to handle all unknown input that might confuse the fault detection system. The aim objective of this work is to use wavelets to increase t...

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Main Authors: Kabbaj, N., Doncescu, A., Dahhou, B., Roux, G.
Format: Conference Proceeding
Language:English
Subjects:
Adaptative systems
Alcoholism
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Fault detection
Filters
Intelligent sensors
Learning and adaptive systems
Noise measurement
Noise robustness
System performance
Testing
Wavelet transforms
White noise
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creator Kabbaj, N.
Doncescu, A.
Dahhou, B.
Roux, G.
description Fault detection and isolation (FDI) is an important issue for safe operation in industrial processes. To avoid false alarms, the FDI scheme must be robust enough to handle all unknown input that might confuse the fault detection system. The aim objective of this work is to use wavelets to increase the robustness of residuals to measurement noise. Our approach is tested in simulation on an alcoholic fermentation process. The faults are modelled as changes in the system parameters and residuals are generated using a set of adaptive observers.
doi_str_mv 10.1109/ISIC.2002.1157789
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subjects Adaptative systems
Alcoholism
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Fault detection
Filters
Intelligent sensors
Learning and adaptive systems
Noise measurement
Noise robustness
System performance
Testing
Wavelet transforms
White noise
title Wavelet based residual evaluation for fault detection and isolation
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