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

Xiao Qin; Xingyi Song; Tong Liu; Hatim Laalej; Zepeng Liu; Yunpeng Zhu; Ligang He

cs.CLApr 7, 2026

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

Authors: Xiao Qin, Xingyi Song, Tong Liu, Hatim Laalej, Zepeng Liu, Yunpeng Zhu, Ligang He

Organizations: Department of Computer Science, University of Warwick, Coventry, UK · Department of Computer Science, University of Sheffield, Sheffield, UK · Advanced Manufacturing Research Centre, University of Sheffield, Sheffield, UK · Shanghai Research Institute for Intelligent Autonomous Systems, State Key Laboratory of Autonomous Intelligent Unmanned Systems, Department of Control Science and Engineering, Tongji University, Shanghai, China ·

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.

Figures & tables

Figure 1: Training through token prediction: from ASR, LMs, and VLMs to LoRM. LoRM extends this paradigm to rotating machinery by predicting discrete future signal tokens from observed multi-sensor context.

Language modelling	LoRM for rotating machinery
Word / token	Discretised target token $y^{(c)}\in\{1,\ldots,K\}$ for channel $c$
Vocabulary	Per-channel codebook of $K$ clusters learned by $k$ -means
Context	Continuous multi-sensor context segment $\mathbf{X}_{\text{ctx}}$
Token prediction	Predict $p(y^{(c)}\mid\mathbf{X}_{\text{ctx}})$ for each channel
Sequence (sentence)	Flattened context sequence from all channels (multi-sensor context)
Self-attention	Models interactions across time and channels within the unified sequence

Table I: Analogy between language-modelling concepts and LoRM.

Figure 2: Overall architecture and learning mechanism of LoRM. (a) A multi-channel signal window is divided into a context segment and a target segment, and the context segment is partitioned and flattened into a multi-sensor patch sequence. (b) The patch sequence is projected into the hidden space of a pre-trained language model with positional embeddings. (c) A partially fine-tuned pre-trained language model encodes temporal and cross-channel dependencies. (d) The contextualised patch features are mean-pooled into a global representation and transformed into channel-wise token probability distributions. (e) The target segment of each channel is independently tokenised by $k$ -means clustering to generate ground-truth tokens. (f) A cross-entropy objective is applied between the predicted distributions and the generated tokens for self-supervised training.

Figure 3: Overview of the TCM case study. (a) Front view of the DMU 40 eVo CNC milling machine and the deployed sensing system, including a power sensor, tri-axis spindle accelerometer, a tri-axis plate accelerometer, a tri-axis force sensor and a microphone. (b) Examples of the collected multi-modal sensor signals. (c) Illustration of the dynamic milling strategy. (d) Evolution of the maximum tool wear measured from the four-flute cutters for Tool 1 to 3 using a microscope.

Metric	Notation	Formula
Accuracy	ACC	$\frac{TP+TN}{TP+TN+FP+FN}$
Precision	P	$\frac{TP}{TP+FP}$
Recall	R	$\frac{TP}{TP+FN}$
F1-score	F1	$2\times\frac{P\times R}{P+R}$
False positive rate	FPR	$\frac{FP}{FP+TN}$

Table II: Classification Performance Metrics

Method	Backbone	Test Tool	Train on Tool1						Train on Tool2						Train on Tool3
			Acc	P	R	F1	FPR	Error $\downarrow$ ( $\mu$ m)	Acc	P	R	F1	FPR	Error $\downarrow$ ( $\mu$ m)	Acc	P	R	F1	FPR	Error $\downarrow$ ( $\mu$ m)
Traditional	Statistics [ 6 ]	Tool1	0.98	1.00	0.91	0.95	0.00	1.21	0.96	1.00	0.79	0.88	0.00	5.62	0.79	0.00	0.00	0.00	0.01	185.95
		Tool2	0.92	1.00	0.54	0.71	0.00	79.00	0.93	1.00	0.60	0.75	0.00	35.18	0.87	1.00	0.31	0.48	0.00	70.96
		Tool3	0.88	1.00	0.53	0.70	0.00	66.23	0.98	1.00	0.94	0.97	0.00	11.26	0.99	1.00	0.95	0.97	0.00	1.15
		Average	0.93	1.00	0.66	0.79	0.00	48.81	0.96	1.00	0.78	0.87	0.00	17.35	0.88	0.67	0.42	0.48	0.00	86.02
PLM -based	GPT2 (Pretrained) [ 22 ]	Tool1	0.90	0.68	1.00	0.81	0.13	23.99	0.97	1.00	0.86	0.92	0.00	1.96	0.84	1.00	0.27	0.42	0.00	7.51

Table III: Cross-Tool Prediction Performance of Different Methods

Panel of Figure 4 — Figure 4: Field-test results of LoRM under cross-tool settings: (a) trained on T1 and tested on T1, T2, and T3; (b) trained on T2 and tested on T1, T2, and T3; and (c) trained on T3 and tested on T1, T2, and T3.

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Spectral Aliasing Pretext: A novel task for Self-Supervised fault diagnosis in rotating machinery

Victor Gialis, Maxime Metz, David Esteve, Abdenour Soualhi

LASPI, Univ. Jean Monnet, Roanne, 42300, France · Pellenc ST, Applied Research Group, Pertuis, 84120, France · LabCom Aioly, Artificial Intelligence and Optics Laboratory, Montpellier, 34196, France+1

Self-Supervised Time Series Representation Learning Fault Diagnosis

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Deep learning is a new way for machinery fault diagnosis but requires extensive labeled data, a scarce resource in industrial settings. We propose Spectral Aliasing Pretext (SAP), a self-supervised learning method that pretrains models on unlabeled vibration data by exploiting spectral aliasing. We deliberately undersample signals to create folded spectrum, then train a Transformer to reconstruct the original unfolded spectrum. This pretext task forces the model to learn frequency-domain invariants characteristic of mechanical faults, without potentially destructive augmentations. Experiments on the CWRU dataset show that SAP learns stable and highly discriminative representations. In a linear probing setting, SAP quickly achieves very high classification performance with only a small fraction of labeled data and low variance. In contrast, full fine-tuning, including fully supervised training, does not lead to more stable or better results. Overall, these findings suggest that SAP combined with linear probing can be more effective and reliable than fully supervised training for fault diagnosis with limited labeled data.

Abstract

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