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Supplementary Results: Contextualizing Timed Automata via Knowledge Graphs for Agentic LLM-Based Fault Diagnosis

This repository contains the supplementary results for the paper:

Contextualizing Timed Automata via Knowledge Graphs for Agentic LLM-Based Fault Diagnosis
Tom Westermann, Felix Gehlhoff, Alexander Fay

Overview

The paper proposes a two-stage agentic diagnosis pipeline for fault diagnosis in Cyber-Physical Production Systems (CPPS). A learned timed automaton and its detected anomalies are integrated with the physical plant model into a semantically enriched knowledge graph. Two LLM-based agents operate on this knowledge graph:

  1. The State Description Agent generates natural-language descriptions for each automaton state and for the overall production process of each module.
  2. The Syndrome Diagnosis Agent identifies root causes of detected anomaly syndromes by reasoning over the augmented knowledge graph.

The approach is evaluated on the HaiCPPS benchmark across ten system configurations (DS1–DS10) of increasing complexity, comprising one to four production modules (Mixing, Distillation, Filter, Bottling). All agents use Claude Opus 4.6 at default temperature without task-specific fine-tuning.

Directory Structure

Published_Results/
├── KnowledgeBase/          # Knowledge graph, ontologies, and plant model
├── ProcessDescriptions/    # Module-level process descriptions (State Description Agent)
├── StateDescriptions/      # Per-state natural-language descriptions (State Description Agent)
└── SyndromeDiagnosis/      # Diagnosis reports and aggregated results (Diagnosis Agent)

Each subfolder contains its own README with detailed descriptions of the files it contains.

HaiCPPS Configurations

Dataset Modules # Variables # GT States Flow Type
DS1 Mixing 17 8 convergent
DS2 Distillation 17 6 divergent
DS3 Filtering 8 4 linear
DS4 Bottling 10 5 linear
DS5 Mixing + Bottling 27 13 convergent
DS6 Distillation + Bottling + Bottling 37 16 divergent
DS7 Filter + Bottling 18 9 linear
DS8 Mixing + Filter + Bottling 35 17 convergent
DS9 Filter + Distillation + Bottling + Bottling 45 20 divergent
DS10 Mixing + Distillation + Mixing + Bottling 61 27 conv./div./conv.

Contact

Tom Westermann — tom.westermann@hsu.hamburg
Institute of Automation Technology, Helmut Schmidt University, Hamburg, Germany