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Intelligent bearing structure and temperature field analysis based on finite element simulation for sustainable and green manufacturing

Author

Listed:
  • Jinhai Chen

    (Nantong University)

  • Wenyuan Zhang

    (Nantong University)

  • Heng Wang

    (Nantong University)

Abstract

Intelligent manufacturing is a new mode and trend of sustainable manufacturing development. It optimizes the design and manufacturing process of products and greatly reduces the consumption of resources and energy by virtue of the huge potential of computer modeling and simulation and information communication technology. Under the background of intelligent manufacturing, intelligent bearing is proposed on the basis of traditional bearing products. Intelligent bearing is one of the research and development directions of high-end bearings at home and abroad. Embedded test technology is one of the research fields of intelligent bearings at present. Firstly, based on the finite element model, the structural design problems related to the integration of rolling bearing and sensor are studied. According to the load distribution of bearing, the optimal slot position of bearing is determined. Considering the influence of reducing the bearing capacity, the influence of axial and radial slot ways of bearing outer ring on the maximum deformation and stress of bearing outer ring is studied, and the bearing outer ring is analyzed The relationship between the maximum deformation, maximum stress and the groove size of the outer ring of the bearing is determined to provide the basis for the selection and design of the sensor module. Secondly, according to the friction moment formula, the total heat of the bearing is calculated. Based on the workbench, the temperature field cloud distribution model of the rolling bearing is established to analyze the changes of the speed, the radial load and the temperature of the inner and outer ring of the bearing Finally, according to the slot position and slot size, select the appropriate sensor and rolling bearing integration to achieve the real-time monitoring of the operation state of the bearing.

Suggested Citation

  • Jinhai Chen & Wenyuan Zhang & Heng Wang, 2021. "Intelligent bearing structure and temperature field analysis based on finite element simulation for sustainable and green manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 745-756, March.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01702-x
    DOI: 10.1007/s10845-020-01702-x
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    References listed on IDEAS

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    1. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    2. D. Benmahdi & L. Rasolofondraibe & X. Chiementin & S. Murer & A. Felkaoui, 2019. "RT-OPTICS: real-time classification based on OPTICS method to monitor bearings faults," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2157-2170, June.
    3. Cong Wang & Meng Gan & Chang’an Zhu, 2017. "Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1377-1391, August.
    4. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
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