FactoryNet is the first universal pretraining corpus for industrial time-series data with a shared S-E-F-C schema that supports cross-embodiment transfer and competitive anomaly detection.
A hybrid prognostics approach for estimating remaining useful life of rolling element bearings
3 Pith papers cite this work, alongside 1,780 external citations. Polarity classification is still indexing.
3
Pith papers citing it
1,780
external citations · external index
years
2026 3representative citing papers
On a 10-bearing PHME subset, a residual-calibrated fusion model hits ~0.15 normalized MAE and 0.90 average 90% coverage under leave-regime-out splits, while conditional diagnostics expose 0.666 coverage and raw-channel-loss collapse.
Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.
citing papers explorer
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FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
FactoryNet is the first universal pretraining corpus for industrial time-series data with a shared S-E-F-C schema that supports cross-embodiment transfer and competitive anomaly detection.
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Empirical Calibration and Conditional-Reliability Diagnostics for Bearing RUL Prediction under Operating-Regime Shift
On a 10-bearing PHME subset, a residual-calibrated fusion model hits ~0.15 normalized MAE and 0.90 average 90% coverage under leave-regime-out splits, while conditional diagnostics expose 0.666 coverage and raw-channel-loss collapse.
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Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity
Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.