Loading...
Loading...

Go to the content (press return)

Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Author
Zurita, D.; Sala, E.; Cariño , J.A.; Delgado Prieto, M.; Ortega, J.A.
Type of activity
Presentation of work at congresses
Name of edition
21st IEEE International Conference on Emerging Technologies & Factory Automation
Date of publication
2016
Presentation's date
2016-09
Book of congress proceedings
21th IEEE Conference on Emerging Technologies and Factory Automation (ETFA): September 6-9, 2016, Berlin
First page
2
Last page
9
Publisher
IEEE Press
DOI
https://doi.org/10.1109/ETFA.2016.7733534 Open in new window
Repository
http://hdl.handle.net/2117/97192 Open in new window
Abstract
Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. I...
Citation
Zurita, D., Sala, E., Cariño , J.A., Delgado Prieto, M., Ortega, J.A. Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA): 6-9 Sept. 2016". Berlin: IEEE Press, 2016.
Keywords
condition monitoring, copper rod industrial plant, critical industrial signal time-series forecasting, critical signal modeling, day-to-day operation, industrial condition monitoring approach, industrial manufacturing plant, industrial process monitoring, internal dynamics, knowledge acquisition, knowledge extraction, operating point codification, process monitoring, production engineering computing, productive process, recurrent neural nets, recurrent neural network, reliability problem, self-organising feature maps, self-organizing map
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

Participants

Attachments