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Maintenance scheduling optimization for industrial centrifugal pumps

Author

Listed:
  • Augusto Bianchini

    (University of Bologna)

  • Marco Pellegrini

    (University of Bologna)

  • Jessica Rossi

    (University of Bologna)

Abstract

Centrifugal pumps are widely used in industry and their damage can cause time losses and production and service level reduction. Due to varying operating conditions, preventive maintenance is the typical strategy to avoid unexpected failures of pumps, but it may involve unnecessary interventions and costs. The paper aims to develop and apply a Condition-Based Maintenance (CBM) procedure on centrifugal pumps to assist decision-making in scheduling maintenance. Through the monitoring of pump state, it would be possible to plan an optimal scheduling of maintenance actions in terms of time, costs and spare parts procurement. The proposed procedure is based on vibration monitoring. Instrumentation, measurement locations, acquisition parameters and pump operating conditions were defined, according to ISO 10816-7 (2009). The method was previously developed for submersible well pumps and now is applied to horizontal and vertical surface centrifugal pumps. Several vibration tests were carried on centrifugal pumps in the plants of the Italian utility Company: Publiacqua S.p.A. (Italy). Pumps under study were selected with varying size and operation time to provide the vibration trend with pump characteristics and operating time, that is a novelty in literature and in practical application. Contrary to what expected by ISO 10816-7 (2009), the paper shows that vibration is influenced by several parameters (e.g. power, pump orientation and rotational speed), which make the ISO standard approach not quite appropriate for centrifugal pumps. As in the proposed method, monitoring the vibrational trend and its variations with time could ensure a more effective CBM strategy.

Suggested Citation

  • Augusto Bianchini & Marco Pellegrini & Jessica Rossi, 2019. "Maintenance scheduling optimization for industrial centrifugal pumps," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 848-860, August.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:4:d:10.1007_s13198-019-00819-4
    DOI: 10.1007/s13198-019-00819-4
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    References listed on IDEAS

    as
    1. Su, Chun & Wang, Xiaolin, 2016. "A two-stage preventive maintenance optimization model incorporating two-dimensional extended warranty," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 169-178.
    2. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    3. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    4. Buddha Kishore & M. R. S. Satyanarayana & K. Sujatha, 2016. "Efficient fault detection using support vector machine based hybrid expert system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 34-40, December.
    5. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    6. Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Eiriksson, Egill Thor, 2016. "Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes," Renewable Energy, Elsevier, vol. 91(C), pages 90-106.
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    Cited by:

    1. Rasool Motahari & Yasser Saeidi Sough & Hamed Aboutorab & Morteza Saberi, 2021. "Joint optimization of maintenance and inventory policies for multi-unit systems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(3), pages 587-607, June.
    2. Augusto Bianchini & Jessica Rossi & Marco Pellegrini, 2019. "Overcoming the Main Barriers of Circular Economy Implementation through a New Visualization Tool for Circular Business Models," Sustainability, MDPI, vol. 11(23), pages 1-33, November.

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