将工业传感器信号转为自然语言，实现可解释的零样本故障诊断
S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis
- 用信号转语义算子将时序数据生成语言描述
- 基于历史维修文档和动态查询实现多轮诊断
- 支持人工反馈迭代，适合工业运维场景
故障诊断对工业系统安全运行至关重要。传统模型输出抽象的异常分数或故障类别，难以回答‘为什么’或‘如何修复’等关键问题。尽管大语言模型具备强泛化与推理能力，但其在离散文本语料上训练，与高维时序工业信号存在语义鸿沟。为此，我们提出信号到语义故障诊断（S2S-FDD）框架，通过两项创新：首先设计信号转语义算子，将抽象时序信号转化为包含趋势、周期性和偏差的语言摘要；其次构建多轮树状诊断机制，基于历史维护文档并动态查询额外信号进行诊断。该框架还支持人机协同反馈以持续优化。在多相流过程数据集上的实验验证了方法在可解释零样本故障诊断中的可行性和有效性。
原文摘要 · Abstract (English)
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.
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