Abstract
The wear of mechanical components and its eventual failure in manufacturing plants, results in companies spending time and resources that, if not scheduled with predictive or preventive maintenance, can lead to production deviation or loss with dire consequences. Nonetheless, current modern plants are frequently highly monitored and automated, generating great quantities of data from a variety of sensors and actuators. Using this raw data, Machine Learning (ML) techniques can be implemented to achieve predictive maintenance. In this work, a method to predict and estimate the wear of a valve using the data related to an opening valve in Iberian Lube Base Oils Company, S.A. (ILBOC) is proposed. The dataset has been built from sensor data in the plant and formatted to use with Tensorflow package in Python. Then a Multi-Layer Perceptron (MLP) neural network is used to predict and estimate the ideal behavior of the valve without wear and a Recurrent Neural Network (RNN) to predict the real behavior of the valve with wear. Comparing both predictions an estimation of the valve wear is realized. Finally, this work closes with a discussion on an early alert system to schedule and plan the replacement of the valve, conclusions and future research.
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Acknowledgments
Thanks to ILBOC and Fundación Séneca for supporting and funding this research work. This work has been partially supported by Spanish National projects AES2017- PI17/00771 and AES2017-PI17/00821 (Instituto de Salud Carlos III).
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Fernández-García, ME., Larrey-Ruiz, J., Ros-Ros, A., Figueiras-Vidal, A.R., Sancho-Gómez, JL. (2019). Machine-Health Application Based on Machine Learning Techniques for Prediction of Valve Wear in a Manufacturing Plant. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_38
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DOI: https://doi.org/10.1007/978-3-030-19651-6_38
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