Abstract
In industrial plants, the analysis of signals provided by monitoring sensors is a difficult task due to the high dimensionality of the data. This work proposes the use of Autoassociative Neural Networks trained with a Modified Robust Method in an online monitoring system for fault detection and self-correction of measurements generated by a large number of sensors. Unlike the existing models, the proposed system aims at using only one neural network to reconstruct faulty sensor signals. The model is evaluated with the use of a database containing measurements collected by industrial sensors that control and monitor an internal combustion engine. Results show that the proposed model is able to map and correct faulty sensor signals and achieve low error rates.
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Sanchez, J.R., Vellasco, M., Tanscheit, R. (2012). Measurement Correction for Multiple Sensors Using Modified Autoassociative Neural Networks. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_14
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DOI: https://doi.org/10.1007/978-3-642-32909-8_14
Publisher Name: Springer, Berlin, Heidelberg
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