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<p>Large language models (LLMs) can support predictive maintenance by reasoning over text-based expert knowledge, but their reliability depends on how such knowledge is structured and retrieved. This work presents an expert-knowledge-grounded LLM inference pipeline that combines Delphi- and FMEA-derived maintenance knowledge, retrieval-augmented prompting, and engineered telemetry summaries. In a multi-robot case study, we evaluate basic knowledge queries, complex diagnostic questions, and telemetry description tasks. Retrieved expert knowledge improves LLM-based diagnostic answers, while a deterministic knowledge-graph reasoner performs best on threshold-driven telemetry questions. The results indicate a complementary design in which graph inference provides traceable rule execution and expert-knowledge-grounded LLMs synthesize diagnostic explanations from selected evidence.</p>

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Enhancing LLM inference with human expert knowledge: a case study on multi-agent robotics fault diagnosis and prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月23日

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  • Yongxu Ren,
  • Felix Deichsel,
  • Jürgen Seiler,
  • André Kaup,
  • Philipp Beckerle

摘要

Large language models (LLMs) can support predictive maintenance by reasoning over text-based expert knowledge, but their reliability depends on how such knowledge is structured and retrieved. This work presents an expert-knowledge-grounded LLM inference pipeline that combines Delphi- and FMEA-derived maintenance knowledge, retrieval-augmented prompting, and engineered telemetry summaries. In a multi-robot case study, we evaluate basic knowledge queries, complex diagnostic questions, and telemetry description tasks. Retrieved expert knowledge improves LLM-based diagnostic answers, while a deterministic knowledge-graph reasoner performs best on threshold-driven telemetry questions. The results indicate a complementary design in which graph inference provides traceable rule execution and expert-knowledge-grounded LLMs synthesize diagnostic explanations from selected evidence.