Industrial Tokenization: A Federated Architecture for LLM-Based Health Intelligence

A new paper on arXiv introduces Industrial Tokenization, a conceptual interface for integrating heterogeneous industrial data into large language models (LLMs). The approach transforms source-specific analytical outputs from condition monitoring, SCADA systems, maintenance records, inspection results, and prognostic models into structured, machine-interpretable units called Industrial Tokens. Unlike numerical tokens for raw time-series data, these tokens represent domain-grounded evidence. The goal is to enable cross-source reasoning while preserving interpretability, traceability, and adaptability to equipment and data changes. The paper proposes a federated architecture for industrial evidence integration, addressing challenges of differing structure, temporal resolution, physical meaning, and reliability across information sources. This work is relevant to industrial health management and the application of LLMs in engineering domains.