1st Edition
Digital Twin for Gear Wear Monitoring and Prediction
Chapter 1: Introduction
1.1 Introduction
1.2 Research Goals
1.3 Structure of This Book
Chapter 2: Literature Review
2.1 Gear Wear
2.1.1 Gear Wear Modes
2.1.2 Differences between Abrasive Wear and Fatigue Pitting
2.2 Gear Wear Effects on Vibrations of Gear Systems
2.3 Use of Vibration Features for Gear Wear Monitoring
2.3.1 Vibration Feature-based Wear Evolution Tracking
2.3.2 Vibration Feature-based Wear Mechanism Identification
2.4 Model-Based Gear Wear Monitoring Techniques
2.4.1 Dynamic Models of Spur Gearboxes
2.4.2 Tribological (Wear) Models for Monitoring Wear Depth and Pitting Density
2.4.3 Integration of Dynamic and Tribological Models for Gear Wear Monitoring
2.5 Wear Prediction Techniques
2.5.1 Prediction of Tooth Profile Change from Abrasive Wear
2.5.2 Prediction of Surface Pitting Propagation
2.5.3 Research Gaps
2.6 Summary
Chapter 3: Methodology
3.1 The Overall Strategy of the Vibration-based Integrated System for Gear Wear Monitoring
3.2 Experimental Research Facilities and Test Programs
3.2.1 Spur Gearbox at University of New South Wales
3.2.2 Wear Tests and Data Collection
3.3 Gear Wear Mechanism Identification and Wear Evolution Tracking Using Cyclostationary Properties of Vibrations (Objective 1)
3.4 Model-based Gear Wear Monitoring and Prediction Methodology
3.4.1 Spur Gearbox Dynamic Model Development (Objective 2)
3.4.2 Monitoring and Prediction of Tooth Profile Changes from Abrasive Wear (Objective 3)
3.4.3 Monitoring and Prediction of Surface Pitting Propagation and Tooth Profile Change from Abrasive Wear Using a Digital Twin Approach (Objective 4)
Chapter 4: Identification of Gear Wear Mechanisms and Tracking Wear Evolution Using Cyclostationary Properties of Measured Vibrations
4.1 Introduction
4.2 Hypothesis and Proposed Vibration-based Approach for Gear Wear Identification
4.2.1 Surface Feature Differences and Their Effects on Sliding Vibrations
4.2.2 Hypothesis for Wear Mechanism Identification
4.2.3 Vibration-based Approach for Wear Mechanism Identification
4.3 Observations in Gear Systems
4.3.1 Tribological Features Used to Describe Fatigue Pitting and Abrasive Wear Propagation
4.3.2 Theory of Vibration-based Wear Mechanism Identification Techniques
4.3.3 Observation Results
4.4 A New Vibration-based Procedure for Comprehensive Gear Wear Monitoring: Mechanism Identification and Severity Tracking
4.5 Summary
Chapter 5: Dynamic Model Development
5.1 Introduction
5.2 Dynamic Model Structure
5.2.1 Meshing Stiffness and Damping Coefficient of Gear System
5.2.2 Geometric Transmission Error
5.2.3 Dynamic Simulation Process
5.3 Model Validation and Calibration
5.4 Summary
Chapter 6: Monitoring and Prediction of Tooth Profile Changes during Wear Progression
6.1 Introduction
6.2 Methodology for Monitoring and Predicting Tooth Profile Change from Wear
6.2.1 The Proposed Vibration-based Approach for Monitoring and Predicting Tooth Profile Change
6.2.2 Dynamic Model
6.2.3 Vibration-based Approach for Wear Mechanism Identification
6.2.4 Updating Methodology
6.3 Tooth Profile Change Prediction Results
6.3.1 Dry Test
6.3.2 Lubricated Test
6.4 Summary
Chapter 7: Development of a Digital Twin Approach for Monitoring and Prediction of Surface Pitting and Tooth Profile Changes
7.1 Introduction
7.2 Relationships of Vibration Features and Wear Features
7.3 Methodology for Monitoring and Predicting Surface Pitting and Tooth Profile Change
7.3.1 Structure of the Proposed Vibration-based Surface Degradation Prediction Methodology
7.3.2 Wear Models: Modelling Surface Pitting Behaviours
7.3.3 Model Updating Procedures using Measured Vibrations
7.4 Test and Results
7.4.1 Monitoring and Prediction of Surface Pitting and Mild Tooth Profile Change during the Lubricated Test
7.4.2 Monitoring and Prediction of Severe Tooth Profile Change during the Dry Test
7.5 Summary
Chapter 8: Conclusions and Future Work
8.1 Summary of Findings and Contribution to Research
8.2 Recommendations for Future Work
Biography
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.
