Prospects for the Use of Free Software Systems of Corporations in the Automotive and Oil and Gas Industries

Enterprises that practice centralized repair of aggregates and systems according to technical condition (CRTC) have become widespread in the structure of repair enterprises of special equipment in the oil and gas industry. The growing interest in modular and distributed approaches to the design and management of technological production structures creates new problems. One of the main problems that has not yet been properly solved is the monitoring, diagnostics and distribution of technological processes of CRTC in specific production systems. In this paper, the analysis of generalized methods for forming the quantity and composition of repair work complexes for various ways of implementing centralized repair of car aggregates according to technical condition is carried out and recommendations for choosing the most rational of them for various organizational and management systems are developed. Three methods of optimization of technological processes of CRTC aggregates of special equipment were subjected to efficiency comparison. In this case, the performance indicators were evaluated on a five-point scale. The apparatus of multidimensional cluster analysis is used as a mathematical optimization tool. In the work carried out by order of PJSC “Surgutneftegaz”, the analysis of generalized methods for forming the quantity and composition of repair work complexes (RWC) for various forms of organization of CRTC of car aggregates was carried out and recommendations were developed for choosing the most rational of them, taking into account the appropriate structure of the production process. It is taken into account that the parameters of learning algorithms are heuristic in nature, depending on the types of artificial neural networks.

• Record URL:
  • https://doi.org/10.1007/978-3-030-96380-4_174
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9783030963798
• Supplemental Notes:
  • © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
• Corporate Authors:

  Springer International Publishing

  ,    
• Authors:
  • Krasovskaya, Nina
  • Sycheva, Anastasia
  • Krasovskaya, Olga
  • Leshchev, Anton
• Conference:
  • International Scientific Siberian Transport Forum TransSiberia - 2021
  • Date: 2021-5-11 to 2021-5-14
• Publication Date: 2022-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1579-1585
• Monograph Title: International Scientific Siberian Transport Forum TransSiberia - 2021: Volume 1
• Serial:
  • Lecture Notes in Networks and Systems
  • Volume: 402
  • Publisher: Springer Cham
  • ISSN: 2367-3370
  • Serial URL: https://www.springer.com/series/15179

Subject/Index Terms

• TRT Terms: Equipment maintenance; Gas industry; Machine learning; Optimization; Petroleum industry; Production methods; Repairing
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Vehicles and Equipment;

Filing Info

• Accession Number: 01961779
• Record Type: Publication
• ISBN: 9783030963798
• Files: TRIS
• Created Date: Jul 24 2025 11:31AM