Yongbo Li

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...

An integrated intelligent diagnosis framework for harmonic drives under fast-varying operating conditions

Harmonic drives are indispensable for precision motion in industrial robots and space manipulators, yet their reliability is critically challenged under fast-varying operating conditions. The complex rigid-flexible coupling dynamics and resulting non-stationary vibration signals render traditional fault diagnosis metho...

Dynamic knowledge graph enhanced large language model with cascade relation extraction optimized for aviation equipment fault diagnosis