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Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks

Author

Listed:
  • Andrey I. Vlasov

    (Bauman Moscow State Technical University)

  • Pavel V. Grigoriev

    (Bauman Moscow State Technical University)

  • Aleksey I. Krivoshein

    (Bauman Moscow State Technical University, LLC "Konnekt")

  • Vadim A. Shakhnov

    (Bauman Moscow State Technical University)

  • Sergey S. Filin

    (Bauman Moscow State Technical University, LLC "Konnekt")

  • Vladimir S. Migalin

    (LLC "Konnekt")

Abstract

One of the most important problems of creating new and also modernizing and operating the existing industrial equipment is to provide it with technical diagnostic tools. In modern systems, most diagnostic problems are solved by vibration monitoring methods, and they form the basis of this process. For several years already, when creating new responsible equipment, many manufacturers have completed it with monitoring and diagnostic systems, often integrating them functionally with automatic control systems. This paper discusses the methods of servicing industrial equipment, focusing on predictive maintenance, also known as actual maintenance (maintenance according to the actual technical condition).The rationale for the use of wireless systems for data collection and processing is presented. The principles of constructing wireless sensor networks and the data transmission protocols used to collect statistical information on the state of the elements of industrial equipment, depending on the field of application, are analyzed. The purpose of the study is to substantiate the feasibility of using wireless sensor networks as technical diagnostic tools from both economic and technical points of view. The result is the proposed concept of the predictive maintenance system. The paper substantiates the model of optimization of predic-tive repair using wireless sensor networks. This approach is based on minimizing the costs of maintenance of equipment. The presented concept of a system of predictive maintenance on the basis of sensor networks allows real-time analysis of the state of equipment. The approach allows implementing smart management of technologies in companies for ensuring stability of functioning.

Suggested Citation

  • Andrey I. Vlasov & Pavel V. Grigoriev & Aleksey I. Krivoshein & Vadim A. Shakhnov & Sergey S. Filin & Vladimir S. Migalin, 2018. "Smart management of technologies: predictive maintenance of industrial equipment using wireless sensor networks," Post-Print hal-02342832, HAL.
    • Handle: RePEc:hal:journl:hal-02342832
    DOI: 10.9770/jesi.2018.6.2(2)
    Note: View the original document on HAL open archive server: https://hal.science/hal-02342832
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    References listed on IDEAS

    as
    1. Gunnar Prause & Gunnar Prause & Sina Atari, 2017. "On sustainable production networks for Industry 4.0," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 4(4), pages 421-431, June.
    2. Gunnar Prause & Sina Atari, 2017. "On sustainable production networks for Industry 4.0," Post-Print hal-01860909, HAL.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    2. Andrey I. Vlasov & Ivan V. Gudoshnikov & Vladimir P. Zhalnin & Aksultan T. Kadyr & Vadim A. Shakhnov, 2020. "Market for memristors and data mining memory structures for promising smart systems," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 98-115, December.
    3. Andrey I. Vlasov & Boris V. Artemiev & Kirill V. Selivanov & Kirill S. Mironov & Jasur O. Isroilov, 2022. "Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 1-7, May.
    4. Jaroslav Vrchota & Martin Pech & Ivona Švepešová, 2022. "Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic," Agriculture, MDPI, vol. 12(8), pages 1-18, July.

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