S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis

2025-08-22

2025 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)

AI Summary

This work addresses the limited interpretability of existing fault diagnosis models, which often fail to explain why a fault occurs or how to rectify it, and the challenge of directly applying large language models due to the semantic gap between industrial time-series signals and natural language. To bridge this gap, the authors propose S2S-FDD, a novel framework featuring a signal-to-semantic transformation operator that maps temporal characteristics—such as trends, periodicity, and anomalies—into natural language descriptions. Integrated with a multi-round tree-based diagnostic mechanism, the framework fuses historical maintenance records and dynamic signal queries to enable zero-shot, interpretable fault diagnosis. The approach supports human-in-the-loop feedback for iterative refinement and demonstrates strong effectiveness and practicality in zero-shot settings on multiphase flow industrial data.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Fault diagnosis is critical for the safe operation of industrial systems. Conventional diagnosis models typically produce abstract outputs such as anomaly scores or fault categories, failing to answer critical operational questions like "Why" or "How to repair". While large language models (LLMs) offer strong generalization and reasoning abilities, their training on discrete textual corpora creates a semantic gap when processing high-dimensional, temporal industrial signals. To address this challenge, we propose a Signals-to-Semantics fault diagnosis (S2S-FDD) framework that bridges high-dimensional sensor signals with natural language semantics through two key innovations: We first design a Signal-to-Semantic operator to convert abstract time-series signals into natural language summaries, capturing trends, periodicity, and deviations. Based on the descriptions, we design a multi-turn tree-structured diagnosis method to perform fault diagnosis by referencing historical maintenance documents and dynamically querying additional signals. The framework further supports human-in-the-loop feedback for continuous refinement. Experiments on the multiphase flow process show the feasibility and effectiveness of the proposed method for explainable zero-shot fault diagnosis.

Problem

• fault diagnosis
• industrial time series
• natural language
• explainability
• zero-shot learning

Innovation

• Signal-to-Semantic
• zero-shot fault diagnosis
• explainable AI
• industrial time series
• large language models