Author

Hanbyeol Park

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#artificial intelligence Preprint Sep 2026

Factorized axis convolutional gated recurrent unit with dynamic adaptive pooling for remaining useful life prediction of rolling bearings

Convolutional neural networks (CNN) are widely used to predict the remaining useful life (RUL) of rolling bearings from time-frequency representations (TFRs) of vibration signals. However, during degradation, characteristic structures in TFRs align predominantly along the frequency or time axis, making it challenging f...

Hanbyeol Park, Jungho Choo, Hyerim Bae · 0 citations

Open access Aug 2026

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data spar...

Hanbyeol Park, Sunghyun Sim, K. Park et al. · Journal of Forecasting · 0 citations

Preprint Aug 2026

Local-Global Feature Mixer and Trend-Guided Consistent Learning for Remaining Useful Life Prediction of Rotating Machinery

Degradation process (DP) modeling is widely used for remaining useful life (RUL) prediction, particularly when run-to-failure data are limited. Neural networks can present complex degradation trajectories without prescribing a fixed degradation function; however, recursive health indicator (HI) forecasting is prone to...

Hanbyeol Park, Hyerim Bae · 0 citations