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Optimization of Low-Cost Data Acquisition Equipment Applied to Bearing Condition Monitoring

Author

Listed:
  • César Ricardo Soto-Ocampo

    (Railway Technology Research Center (Centro de Investigación en Tecnología Ferroviaria-CITEF), Mechanical Engineering Department, Universidad Politécnica de Madrid, 2 José Gutiérrez Abascal Street, 28006 Madrid, Spain)

  • Joaquín Maroto

    (Railway Technology Research Center (Centro de Investigación en Tecnología Ferroviaria-CITEF), Mechanical Engineering Department, Universidad Politécnica de Madrid, 2 José Gutiérrez Abascal Street, 28006 Madrid, Spain)

  • Juan David Cano-Moreno

    (Escuela Técnica Superior de Ingeniería y Diseño Industrial, Universidad Politécnica de Madrid, 28012 Madrid, Spain)

  • José Manuel Mera

    (Railway Technology Research Center (Centro de Investigación en Tecnología Ferroviaria-CITEF), Mechanical Engineering Department, Universidad Politécnica de Madrid, 2 José Gutiérrez Abascal Street, 28006 Madrid, Spain)

Abstract

The development of low-cost data acquisition equipment is relevant in the increasingly automated industry of today. This study presents the optimization of low-cost data acquisition equipment performance to achieve acquisition speeds of 200 kHz. This was possible by evaluating two essential aspects: considering the influence of the power supplied by the power source and changing the type of data used from “Double” to “uint”. This equipment was validated through the acquisition of known waves and vibration signals from a bearing test bench. The frequency component was satisfactorily identified in each case, for both the known waves and the damaged bearing components. This demonstrated the viability of developing low-cost data acquisition equipment that can be implemented to monitor bearing condition.

Suggested Citation

  • César Ricardo Soto-Ocampo & Joaquín Maroto & Juan David Cano-Moreno & José Manuel Mera, 2023. "Optimization of Low-Cost Data Acquisition Equipment Applied to Bearing Condition Monitoring," Mathematics, MDPI, vol. 11(16), pages 1-21, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3498-:d:1216347
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    References listed on IDEAS

    as
    1. Zhang, Nan & Deng, Yingjun & Liu, Bin & Zhang, Jun, 2023. "Condition-based maintenance for a multi-component system in a dynamic operating environment," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Zhe Tong & Wei Li & Enrico Zio & Bo Zhang & Gongbo Zhou, 2022. "Online Bearing Fault Diagnosis Based on Packet Loss Influence-Inspired Retransmission Mechanism," Mathematics, MDPI, vol. 10(9), pages 1-18, April.
    3. Nien-Che Yang & Harun Ismail, 2022. "Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions," Mathematics, MDPI, vol. 10(2), pages 1-18, January.
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