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

arXiv: https://arxiv.org/abs/2603.08048 · HTML: https://mlsanity.com/paper/2603.08048/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings](https://mlsanity.com/paper/2104.09079/) (2021)
- [Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model](https://mlsanity.com/paper/2112.08673/) (2021)

## Built on by (most cited first, 1 of 1)

- [When LLMs Sit Above Diagnostic Tools: Unrealized Complementarity in Industrial Fault Diagnosis](https://mlsanity.com/paper/2610.05031/) (2026)

## Abstract (arXiv)

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.