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