Soft Sensors to Monitoring a Multivariate Nonlinear Process Using Neural Networks

In general, industrial processes have a multivariable nature, with multiple inputs and multiple outputs. Such systems are more difficult to monitor and control due to interactions between the input and output variables. Focusing on these issues, the development of soft sensors to monitor multivariat...

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Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2019-02, Vol.30 (1), p.54-62
Main Authors: Monteiro, Nathalia Arthur Brunet, da Silva, Jaidilson Jó, da Rocha Neto, José Sérgio
Format: Article
Language:English
Subjects:
Control
Control and Systems Theory
Data envelopment analysis
Electrical Engineering
Engineering
Mechatronics
Monitoring
Neural networks
Robotics
Robotics and Automation
Sensors
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Summary:In general, industrial processes have a multivariable nature, with multiple inputs and multiple outputs. Such systems are more difficult to monitor and control due to interactions between the input and output variables. Focusing on these issues, the development of soft sensors to monitor multivariate nonlinear processes using neural networks is proposed. Experiments were performed to monitor the pressure and flow values on an experimental platform (fluid transport system) using developed soft sensors. With the monitoring using soft sensor, it is possible to make processes more reliable, with better performance and with less difficulty in detecting and solving possible failures.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-018-00426-x