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.
Automatic feature extraction and construction using genetic programming for rotating machinery fault diagnosis
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.
citing papers explorer
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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.
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EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming
EvoTSC evolves lightweight feature learning models for time series classification via genetic programming with embedded expert knowledge and Pareto tournament selection, outperforming eleven benchmarks on univariate datasets.