Abstract
The prediction of failures in rotating machines is an important issue in industries to improve safety, to reduce the cost of maintenance and to prevent accidents. In this paper a predictive maintenance algorithm, based on the analysis of the orbits shape of the rotor shaft is proposed. It is based on an autonomous image pattern recognition algorithm, implemented by using a Convolutional Neural Network (CNN). The CNN has been designed, by using a suitable database, to recognize the orbits shape, allowing both fault detection and classification.
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Caponetto, R., Rizzo, F., Russotti, L., Xibilia, M.G. (2019). Deep Learning Algorithm for Predictive Maintenance of Rotating Machines Through the Analysis of the Orbits Shape of the Rotor Shaft. In: Benavente-Peces, C., Slama, S., Zafar, B. (eds) Proceedings of the 1st International Conference on Smart Innovation, Ergonomics and Applied Human Factors (SEAHF). SEAHF 2019. Smart Innovation, Systems and Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-22964-1_25
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DOI: https://doi.org/10.1007/978-3-030-22964-1_25
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