Prediction of Temperature Behavior in Hydraulic Circuits

The increasing complexity of products and systems in modern engineering production requires higher demands on the operation and maintenance of systems. For this reason, the role of maintenance can hardly be underestimated, as the maintenance of machinery and equipment is an integral part of any manufacturing process. Maintenance is closely related to the precision of the parts produced and the quality of the manufacturing process. A current trend in Industry 4.0 is predictive maintenance, which enables real-time detection of faulty equipment conditions based on condition monitoring. Utilizing artificial intelligence for predictive analytics can considerably contribute to the prediction of potential failures. Consequently, maintenance costs related to production downtime and equipment repair will be decreased. In this paper, a non-linear autoregressive neural network for real-time prediction of working fluid temperature parameters in a hydraulic circuit is presented. A testing device is also developed to collect experimental data, optimize, and test the proposed prediction model.

• Record URL:
  • https://doi.org/10.1016/j.trpro.2023.11.174
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2352146523004714
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Fedorova, Daria
  • Stenchlák, Vladimír
  • Tlach, Vladimír
  • Kuric, Ivan
  • Wiecek, Dariusz
• Conference:
  • TRANSCOM 2023: 15th International Scientific Conference on Sustainable, Modern and Safe Transport
  • Location: Mikulov , Czech Republic
  • Date: 2023-5-29 to 2023-5-31
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 500-507
• Serial:
  • Transportation Research Procedia
  • Volume: 74
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Equipment maintenance; Hydraulic equipment; Manufacturing; Predictive models; Temperature; Testing
• Subject Areas: Maintenance and Preservation; Transportation (General);

Filing Info

• Accession Number: 01923467
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 30 2024 4:02PM