Digital Twin for Gear Wear Monitoring and Prediction

This book presents recent research developments and integrated methodologies for digital twin gear wear monitoring and remaining useful life prediction for rotating machinery. It describes a comprehensive framework for identifying wear mechanisms, developing dynamic gearbox models, and implementing online monitoring schemes that track the evolution of abrasive wear and fatigue pitting. The methodologies introduced allow for accurate assessment of tooth profile changes and surface integrity without requiring operational stoppage. Simulations and dynamic model implementations in this book are constructed using the MATLAB® and Simulink® software packages. Features: - Gives a systematic investigation of vibration-based techniques to distinguish between fatigue pitting and abrasive wear - Develops an integrated monitoring and prediction framework using a dynamic gear model and wear model - Includes a novel digital-twin approach that regularly updates model coefficients using measured vibration data to ensure prediction accuracy - Discusses the impact of macro- and micro-level wear on dynamic contact forces and vibration characteristics - Provides experimental validation through run-to-failure tests conducted under both dry and lubricated conditions This book is aimed at researchers and graduate students in mechanical engineering, signal processing, machine condition monitoring, and reliability engineering.

Ke Feng is a Full Professor at Xi'an Jiaotong University, is a Marie Curie Fellow, and is ranked among the "Stanford/Elsevier Top 2% Scientists". He earned his bachelor's and master's degrees from the University of Electronic Science and Technology of China and his Ph.D. from the University of New South Wales. He has held positions at renowned institutions such as the University of British Columbia, the National University of Singapore, and Imperial College London. His research areas include digital twins, machine learning, signal processing, fault diagnosis, fatigue, and wear analysis, among others. In 2023, he was awarded the title of "Emerging Leader" by the Royal Physical Society Journal. He currently serves as an Associate Editor and Editorial Board Member for several international journals, including IEEE Transactions on Industrial Informatics, Information Fusion, IEEE Internet of Things Journal, and Structural Health Monitoring. He has led numerous international collaborative projects, including the Horizon Europe, UKRI projects, the National Natural Science Foundation Excellent Young Scientists Fund, and the key projects under the National Key Research and Development Program of China. He has also received the "Second Prize of the China Aviation Science and Technology Award" and the "Second Prize of the Vibration Engineering Society Science and Technology Award". Qing Ni is a Professor at the School of Artificial Intelligence, OPtics and ElectroNics, Northwestern Polytechnical University, China. She earned her Ph.D. degree from the University of Technology Sydney, Australia, in 2023. She worked at the University of Technology Sydney, Australia from 2023 to 2025. Her main research interests include large language models, digital twins, signal processing, prognostics and health management. Currently, she serves as Associate Editor or Editorial Board Member for IEEE Transactions on Industrial Informatics, Engineering Applications of Artificial Intelligence, Neurocomputing, Journal of Intelligent Manufacturing, IEEE Transactions on Instrumentation & Measurement, IEEE Sensors Journal. She also holds positions as Youth Editor and Guest Editor for several journals, in addition to chairing sessions at international conferences. She is recognized as the Emerging Leader by the Measurement Science and Technology journal and World's Top 2% Scientists by Stanford University in 2024. Hanbin Zhou is currently a Ph.D. candidate in the School of Mechanical Engineering at Xi'an Jiaotong University, China. He received an M.S. degree in Mechanical Engineering from Central South University in 2025. His current research interests focus on cognitive digital twins, machine condition monitoring, signal processing, and artificial intelligence. He has participated in research projects funded by the National Natural Science Foundation of China (NSFC). He holds 4 authorized Chinese invention patents. He has authored research papers in reputable journals such as Measurement Science and Technology. He also serves as a reviewer for leading journals, including IEEE Transactions on Industrial Informatics.

1. Introduction 2. Recent Progress on Vibration-Based Gear Wear Monitoring and Prediction Techniques 3. Identification of Gear Wear Mechanisms and Tracking Wear Evolution 4. : Dynamic Model Development 5. Digital Twin-Based Abrasive Wear Progression Monitoring and Prediction 6. Digital Twin-Based Fatigue Pitting Progression Monitoring and Prediction 7. Conclusion. Appendices.