'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
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. In this paper, a novel industrial condition monitoring approach based on the
combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal
modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating
condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of
recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of
performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version
