Zhuo Zhi

Papers

Total Citations

162

H-Index

About

Dr. Zhuo Zhi is a leading researcher in industrial robotics reliability, with a specialized focus on fault detection and condition monitoring of harmonic reducers—the critical, high-failure-rate components that drive robotic systems. Her work addresses a pressing industrial challenge: ensuring the uninterrupted operation of robots operating under large loads and high torque. Dr. Zhi’s major contributions include pioneering the application of deep learning to this domain. Her most cited paper (127 citations) introduces a novel CNN-LSTM framework paired with a unique denoising algorithm, enabling precise fault detection in harmonic reducers. She further advanced the field by developing a method that leverages acoustic emission signals, detailed in a 2023 work (35 citations), which employs two innovative algorithms to monitor component health in harsh, periodic operating environments. By combining sophisticated signal processing with neural network architectures, Dr. Zhi has significantly improved the accuracy and practicality of predictive maintenance for industrial robots. Her research is instrumental for engineers and scientists seeking to enhance the availability and safety of automated manufacturing systems, directly impacting the reliability of modern industrial infrastructure.