A neural-network approach to fault detection and diagnosis in industrial processes

Using a multilayered feedforward neural-network approach, the detection and diagnosis of faults in industrial processes that requires observing multiple data simultaneously are studied in this paper. The main feature of our approach is that the detection of the faults occurs during transient periods...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 1997-11, Vol.5 (6), p.529-541
Main Authors: Maki, Y., Loparo, K.A.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Chemical engineering
Chemical industry
Chemical processes
Circuit faults
Computer science
control theory
systems
Connectionism. Neural networks
Control theory. Systems
Exact sciences and technology
Fault detection
Fault diagnosis
Filtering
Neural networks
Nonlinear filters
Pattern recognition
Process control. Computer integrated manufacturing
Reactors
Robustness
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Summary:Using a multilayered feedforward neural-network approach, the detection and diagnosis of faults in industrial processes that requires observing multiple data simultaneously are studied in this paper. The main feature of our approach is that the detection of the faults occurs during transient periods of operation of the process. A two-stage neural network is proposed as the basic structure of the detection system. The first stage of the network detects the dynamic trend of each measurement, and the second stage of the network detects and diagnoses the faults. The potential of this approach is demonstrated in simulation using a model of a continuously well-stirred tank reactor. The neural-network-based method successfully detects and diagnoses pretrained faults during transient periods and can also generalize properly. Finally, a comparison with a model-based method is presented.
ISSN:1063-6536
1558-0865
DOI:10.1109/87.641399