Abstract
The evolution of technologies and customer expectations, coupled with market pressure, leads to the search for the permanent optimization of industrial processes. Improving plant performance is imperative to reduce costs and offer innovative, high-quality products delivered on time. In this context, the future industry intends to consolidate the best technologies and equipment to guarantee competitiveness and ever-increasing productivity while ensuring energy efficiency. This study proposes a new model that summarizes a Deep Learning based approach to real-time bearing diagnosis and prognosis. This model supports failure reduction and preventive inspection and replacement actions for the implementation of predictive maintenance. Our study aims to prove our model’s reliability and performance in diagnosis and prognosis in an industrial system.
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Bouyahrouzi, E.M., El Kihel, A., El Kihel, Y., Embarki, S. (2023). Defining and Implementing Predictive Maintenance Based on Artificial Intelligence for Rotating Machines. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-29860-8_83
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DOI: https://doi.org/10.1007/978-3-031-29860-8_83
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