We present LoRM (Language of Rotating Machinery), a self-supervised framework for multi-modal rotating-machinery signal understanding and real-time condition monitoring. LoRM is built on the idea that rotating-machinery signals can be viewed as a machine language: local signals can be tokenised into discrete symbolic units, and their future evolution can be predicted from observed multi-sensor context. Unlike conventional signal-processing methods that rely on hand-crafted transforms and features, LoRM reformulates multi-modal sensor data as a token-based sequence-prediction problem. For each data window, the observed context segment is retained in continuous form, while the future target segment of each sensing channel is quantised into a discrete token. Then, efficient knowledge transfer is achieved by partially fine-tuning a general-purpose pre-trained language model on industrial signals, avoiding the need to train a large model from scratch. Finally, condition monitoring is performed by tracking token-prediction errors as a health indicator, where increasing errors indicate degradation. In-situ tool condition monitoring (TCM) experiments demonstrate stable real-time tracking and strong cross-tool generalisation, showing that LoRM provides a practical bridge between language modelling and industrial signal analysis. The source code is publicly available at https://github.com/Q159753258/LormPHM.

翻译：我们提出LoRM（旋转机械语言）——一个用于多模态旋转机械信号理解与实时状态监测的自监督框架。LoRM建立在"旋转机械信号可视为一种机器语言"的理念之上：局部信号可被标记化为离散符号单元，并通过观测到的多传感器上下文预测其未来演化。与依赖人工设计变换和特征的传统信号处理方法不同，LoRM将多模态传感器数据重构为基于标记的序列预测问题。对于每个数据窗口，保留观测上下文段的连续形式，而将每个传感通道的未来目标段量化为离散标记。随后，通过对通用预训练语言模型进行部分微调，实现工业信号的高效知识迁移，避免从零训练大型模型。最后，通过跟踪标记预测误差作为健康指标来执行状态监测——误差增大指示退化现象。在刀具状态监测（TCM）实验中，该方法展现出稳定的实时跟踪能力与强大的跨刀具泛化性能，表明LoRM在语言建模与工业信号分析之间建立了实用桥梁。源代码已公开于https://github.com/Q159753258/LormPHM。