IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i3d10.1007_s10845-020-01702-x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01702-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01702-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.
    2. Li, Qi & Chen, Liang & Kong, Lin & Wang, Dong & Xia, Min & Shen, Changqing, 2023. "Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    4. Vrignat, Pascal & Kratz, Frédéric & Avila, Manuel, 2022. "Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Ze Wei & Hui Liu & Xuewen Tao & Kai Pan & Rui Huang & Wenjing Ji & Jianhai Wang, 2023. "Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis," Sustainability, MDPI, vol. 15(8), pages 1-29, April.
    6. Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
    7. Youngju Kim & Hoyeop Lee & Chang Ouk Kim, 2023. "A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 529-540, February.
    8. Jannis N. Kahlen & Michael Andres & Albert Moser, 2021. "Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault," Energies, MDPI, vol. 14(20), pages 1-20, October.
    9. Gang Wang & Feng Zhang & Bayi Cheng & Fang Fang, 2021. "DAMER: a novel diagnosis aggregation method with evidential reasoning rule for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 1-20, January.
    10. Tong Zhang & Haowen Chen & Xianqun Mao & Xin Zhu & Lefei Xu, 2024. "A Domain Generation Diagnosis Framework for Unseen Conditions Based on Adaptive Feature Fusion and Augmentation," Mathematics, MDPI, vol. 12(18), pages 1-15, September.
    11. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    12. Mohammed Majid Abdulrazzaq & Nehad T. A. Ramaha & Alaa Ali Hameed & Mohammad Salman & Dong Keon Yon & Norma Latif Fitriyani & Muhammad Syafrudin & Seung Won Lee, 2024. "Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts," Mathematics, MDPI, vol. 12(5), pages 1-42, March.
    13. Abdellatif Elmouatamid & Brian Fricke & Jian Sun & Philip W. T. Pong, 2023. "Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches," Energies, MDPI, vol. 16(12), pages 1-20, June.
    14. Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
    15. Lu-jun Cui & Man-ying Sun & Yan-long Cao & Qi-jian Zhao & Wen-han Zeng & Shi-rui Guo, 2021. "A novel tolerance geometric method based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 799-821, March.
    16. Asif Khan & Hyunho Hwang & Heung Soo Kim, 2021. "Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
    17. Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
    18. 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.
    19. Jialin Li & Xueyi Li & David He & Yongzhi Qu, 2020. "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1899-1916, December.
    20. Chen Zhao & Shichang Du & Jun Lv & Yafei Deng & Guilong Li, 2023. "A novel parallel classification network for classifying three-dimensional surface with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 515-527, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01702-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.