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 wearDevelops an integrated monitoring and prediction framework using a dynamic gear model and wear modelIncludes a novel digital-twin approach that regularly updates model coefficients using measured vibration data to ensure prediction accuracyDiscusses the impact of macro- and micro-level wear on dynamic contact forces and vibration characteristicsProvides experimental validation through run-to-failure tests conducted under both dry and lubricated conditionsThis book is aimed at researchers and graduate students in mechanical engineering, signal processing, machine condition monitoring, and reliability engineering.