LoRM applies self-supervised learning techniques inspired by NLP to condition monitoring of rotating machinery. The approach learns machinery "language" without requiring labeled training data, reducing annotation burden while enabling generalization across equipment types.
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LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring
Self-supervised learning techniques from NLP achieve machinery condition monitoring without labeled data, generalizing across equipment types.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline
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