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Maintenance, traditionally perceived as a reactive cost and a hindrance, poses challenges to efficiency when components succumb to unforeseen breakdowns. In addition to the financial implications, the repair process also incurs substantial time wastage. To overcome these obstacles and achieve enhanced efficiency and cost savings within the manufacturing sector, this paper presents a conceptual study of a technologically advanced predictive maintenance (PdM) approach, particularly in the realm of artificial intelligence-powered digital twins. The effectiveness of these solutions hinges on their data-driven nature, technical feasibility, and acceptance by industry stakeholders.

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Different Facets of Artificial Intelligence-Based Predictive Maintenance for Electric Powertrains

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Ali Serdar Atalay,
  • Ahu Ece Hartavi Karcı,
  • İbrahim Arif,
  • Salih Ergün,
  • Alper Kanak

摘要

Maintenance, traditionally perceived as a reactive cost and a hindrance, poses challenges to efficiency when components succumb to unforeseen breakdowns. In addition to the financial implications, the repair process also incurs substantial time wastage. To overcome these obstacles and achieve enhanced efficiency and cost savings within the manufacturing sector, this paper presents a conceptual study of a technologically advanced predictive maintenance (PdM) approach, particularly in the realm of artificial intelligence-powered digital twins. The effectiveness of these solutions hinges on their data-driven nature, technical feasibility, and acceptance by industry stakeholders.