LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring

TL;DR

LoRM introduces a self-supervised, token-based language model approach for real-time condition monitoring of rotating machinery, leveraging multi-modal sensor data and transfer learning.

Contribution

It reformulates multi-sensor signals as a sequence prediction problem using language models, enabling effective, real-time machinery health assessment without extensive labeled data.

Findings

LoRM achieves stable real-time condition monitoring.

It generalizes well across different tools and sensors.

Token-prediction errors serve as effective health indicators.

Abstract

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.