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<p>A software-defined vehicle (SDV) utilizes a centralized control computer and a new electrical/electronic architecture to enhance vehicle performance, safety, and user experience. Their agile development approach emphasizes a DevOps culture, characterized by iterative and automated software updates and deployments. In this study, we define DevOps with Domain as activities that integrate domain knowledge directly into the development pipeline and discuss a prognostics and health management (PHM) strategy for detecting failures caused by degradation in legacy polymer suspension components, which are indispensable to SDVs, and parts whose polymer properties significantly affect operational performance. Part 1 analyzes the domain knowledge of target components as well as the characteristics of the physical model and failure modes, and Part 2 describes signal processing and feature extraction techniques designed to efficiently explore key features in measurement data related to the degradation and failure of these components.</p> Graphical Abstract <p></p>

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Diagnosis technology for polymer-based chassis components for future mobility: part 1. DevOps with domain

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Yong-Hyun Ryu,
  • Kyung-Woo Lee,
  • Sang-Hyun Moon,
  • Yong-Gwon Jeon,
  • Eun-Il Kim,
  • Kyoung-Soo We,
  • Sung-Wook Hwang,
  • Sang-Won Lee,
  • Dae-Un Sung

摘要

A software-defined vehicle (SDV) utilizes a centralized control computer and a new electrical/electronic architecture to enhance vehicle performance, safety, and user experience. Their agile development approach emphasizes a DevOps culture, characterized by iterative and automated software updates and deployments. In this study, we define DevOps with Domain as activities that integrate domain knowledge directly into the development pipeline and discuss a prognostics and health management (PHM) strategy for detecting failures caused by degradation in legacy polymer suspension components, which are indispensable to SDVs, and parts whose polymer properties significantly affect operational performance. Part 1 analyzes the domain knowledge of target components as well as the characteristics of the physical model and failure modes, and Part 2 describes signal processing and feature extraction techniques designed to efficiently explore key features in measurement data related to the degradation and failure of these components.

Graphical Abstract