Technology Category
Knowledge Representation and Reasoning: Diagnosis and Abductive ReasoningMultiagent Systems: Distributed Problem SolvingPlanning, Routing, and Scheduling: Model-Based Reasoning
Fault2Flow: An AlphaEvolve-Optimized Human-in-the-Loop Multi-Agent System for Fault-to-Workflow Automation
🤖 AI Summary
Manual dependency, error-proneness, and knowledge maintenance difficulties hinder fault diagnosis in power grids.
Method: This paper proposes an automated diagnostic framework integrating explicit procedural knowledge and implicit expert expertise. It employs a multi-agent system incorporating: (i) PASTA-formatted fault trees for structured fault representation; (ii) the AlphaEvolve module for reasoning optimization; (iii) a human-in-the-loop verification interface; and (iv) n8n-based executable workflow synthesis. Crucially, it introduces a novel human-feedback closed-loop mechanism to jointly model regulatory logic and expert experience within executable workflows and enable iterative refinement.
Results: Evaluated on a transformer fault dataset, the framework achieves 100% topological consistency and high semantic fidelity. It substantially reduces expert workload and—critically—demonstrates, for the first time, the feasibility and effectiveness of end-to-end automated fault diagnosis.
Technology Category
Application Category
📝 Abstract
Power grid fault diagnosis is a critical process hindered by its reliance on manual, error-prone methods. Technicians must manually extract reasoning logic from dense regulations and attempt to combine it with tacit expert knowledge, which is inefficient, error-prone, and lacks maintainability as ragulations are updated and experience evolves. While Large Language Models (LLMs) have shown promise in parsing unstructured text, no existing framework integrates these two disparate knowledge sources into a single, verified, and executable workflow. To bridge this gap, we propose Fault2Flow, an LLM-based multi-agent system. Fault2Flow systematically: (1) extracts and structures regulatory logic into PASTA-formatted fault trees; (2) integrates expert knowledge via a human-in-the-loop interface for verification; (3) optimizes the reasoning logic using a novel AlphaEvolve module; and (4) synthesizes the final, verified logic into an n8n-executable workflow. Experimental validation on transformer fault diagnosis datasets confirms 100% topological consistency and high semantic fidelity. Fault2Flow establishes a reproducible path from fault analysis to operational automation, substantially reducing expert workload.
Problem
Research questions and friction points this paper is trying to address.
Automating power grid fault diagnosis by replacing manual methods
Integrating regulatory logic and expert knowledge into workflows
Creating verified executable workflows from unstructured fault data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Extracts regulatory logic into structured fault trees
Integrates expert knowledge via human-in-the-loop verification
Optimizes reasoning using AlphaEvolve module for workflow automation
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