Abstract
Vibration analysis (VA) techniques have aroused great interest in the industrial sector during the last decades. In particular, VA is widely used for rotatory components failure detection, such as rolling bearings, gears, etc. In the present work, we propose a novel data-driven methodology to process vibration-related data, in order to detect rotatory components failure in advance, using spectral data. Vibration related data is first transformed to the frequency domain. Then, a feature called severity is calculated from the spectra. Based on the relation of this feature with respect to the production condition variables, a specific prediction model is trained. These models are used to estimate the thresholds for the severity values. If the real value of the severity exceeds the estimated threshold, the spectra associated to the severity is analyzed thoroughly, in order to determine whether this data shows any failure evidence or not. The proposed data processing system is validated in a real failure context, using data monitored in a paper mill machine. We conclude that a maintenance plan based on the proposed would enable to predict a failure of a rotatory component in advance.
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Acknowledgement
This project was supported by the project 3KIA - Propuesta Integral y Transversal Para El Diseño E Implantación De Sistemas Confiables Basados En Inteligencia Artificial, funded by the Basque Government (Spain) program ELKARTEK, grant number KK-2020/00049.
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Arregi, A., Inza, I., Bediaga, I. (2022). Vibration Analysis for Rotatory Elements Wear Detection in Paper Mill Machine. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_19
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