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<p>This paper presents an enhanced methodology for parameter diagrams (P-Diagrams) designed to support system analysis and sensor integration in condition monitoring. Building on the foundations of the AIAG &amp; VDA and Ford FMEA Handbooks, the proposed approach extends the classical structure by introducing sensor-relevant variables, including SI units, expected measurement ranges, and sensor assignments. It also refines the classification of outputs into intended, unintended, and deviating categories, and provides a&#xa0;structured representation of control and noise factors. The methodology was validated using a&#xa0;screw compressor as a&#xa0;representative system. The case study illustrates how the enhanced P‑Diagram facilitates a&#xa0;detailed functional analysis, supports systematic sensor selection, and improves traceability in documenting system interactions. The results demonstrate the potential of the enhanced P‑Diagram as a&#xa0;practical and scalable modelling tool for condition monitoring. Future research may explore its application in other industrial domains and investigate the use of artificial intelligence and machine learning for automated diagram generation and refinement.</p>

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Enhanced methodology of parameter diagram for system analysis and sensor integration in condition monitoring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Patrick Harfmann,
  • Lennart Kopp,
  • Akash Mangaluru Ramananda,
  • Marcus Liebschner,
  • Markus Kley

摘要

This paper presents an enhanced methodology for parameter diagrams (P-Diagrams) designed to support system analysis and sensor integration in condition monitoring. Building on the foundations of the AIAG & VDA and Ford FMEA Handbooks, the proposed approach extends the classical structure by introducing sensor-relevant variables, including SI units, expected measurement ranges, and sensor assignments. It also refines the classification of outputs into intended, unintended, and deviating categories, and provides a structured representation of control and noise factors. The methodology was validated using a screw compressor as a representative system. The case study illustrates how the enhanced P‑Diagram facilitates a detailed functional analysis, supports systematic sensor selection, and improves traceability in documenting system interactions. The results demonstrate the potential of the enhanced P‑Diagram as a practical and scalable modelling tool for condition monitoring. Future research may explore its application in other industrial domains and investigate the use of artificial intelligence and machine learning for automated diagram generation and refinement.