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 survey of multi-sensor fusion perception for embodied ai: Background, methods, challenges and prospects
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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.