LoRM is a self-supervised framework that models multi-modal rotating machinery signals as token sequences for prediction with fine-tuned language models, using prediction errors to monitor machine health in real time.
A comprehensive review on sensor fusion techniques for localization of a dynamic target in gps-denied environments,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring
LoRM is a self-supervised framework that models multi-modal rotating machinery signals as token sequences for prediction with fine-tuned language models, using prediction errors to monitor machine health in real time.