Xiao-Hui Yang

A degradation representation learning-based LSTM framework for gear remaining useful life prediction

Predicting the useful lifespan of gears is essential for enhancing predictive maintenance reliability and operational efficiency. A novel MDRL-BiLSTM framework is proposed to improve the modeling of gear degradation processes by jointly learning nonlinear degradation characteristics and long-term temporal dependencies...