Yang Liu , Zheng’ang Shan , Haiying Liang , Qingyang Sun , Hui Ma
{"title":"A review of dynamics-based failure modeling and diagnosis techniques for gear systems","authors":"Yang Liu , Zheng’ang Shan , Haiying Liang , Qingyang Sun , Hui Ma","doi":"10.1016/j.mechmachtheory.2025.106166","DOIUrl":null,"url":null,"abstract":"<div><div>Gear systems are critical in industrial and military applications but prone to failures under harsh conditions, leading to economic losses and safety risks. This paper provides a comprehensive review of dynamics-based failure modeling and diagnosis techniques for gear systems. It systematically evaluates three modeling approaches: analytical methods (efficient but limited to simple systems), lumped parameter models (balance efficiency and multi-DOF dynamics), and finite element methods (high accuracy but computationally intensive). Hybrid strategies integrating these methods and machine learning are highlighted to enhance computational efficiency and accuracy. Common failure mechanisms, including cracks, pitting, and wear, are analyzed, emphasizing their effects on time-varying mesh stiffness (TVMS) and vibration characteristics. Signal processing and machine learning techniques are discussed for fault feature extraction and diagnosis, with advanced methods like variational modal decomposition and AI-augmented models demonstrating superior performance. Challenges in real-time diagnostics, model generalizability, and coupled failure analysis are identified. Future directions propose hybrid AI-physics models, digital twins, and multi-scale frameworks to improve predictive maintenance. This review bridges theoretical insights and practical applications, offering a foundation for advancing gear system reliability and intelligent fault diagnosis.</div></div>","PeriodicalId":49845,"journal":{"name":"Mechanism and Machine Theory","volume":"215 ","pages":"Article 106166"},"PeriodicalIF":4.5000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanism and Machine Theory","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0094114X25002551","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Gear systems are critical in industrial and military applications but prone to failures under harsh conditions, leading to economic losses and safety risks. This paper provides a comprehensive review of dynamics-based failure modeling and diagnosis techniques for gear systems. It systematically evaluates three modeling approaches: analytical methods (efficient but limited to simple systems), lumped parameter models (balance efficiency and multi-DOF dynamics), and finite element methods (high accuracy but computationally intensive). Hybrid strategies integrating these methods and machine learning are highlighted to enhance computational efficiency and accuracy. Common failure mechanisms, including cracks, pitting, and wear, are analyzed, emphasizing their effects on time-varying mesh stiffness (TVMS) and vibration characteristics. Signal processing and machine learning techniques are discussed for fault feature extraction and diagnosis, with advanced methods like variational modal decomposition and AI-augmented models demonstrating superior performance. Challenges in real-time diagnostics, model generalizability, and coupled failure analysis are identified. Future directions propose hybrid AI-physics models, digital twins, and multi-scale frameworks to improve predictive maintenance. This review bridges theoretical insights and practical applications, offering a foundation for advancing gear system reliability and intelligent fault diagnosis.
期刊介绍:
Mechanism and Machine Theory provides a medium of communication between engineers and scientists engaged in research and development within the fields of knowledge embraced by IFToMM, the International Federation for the Promotion of Mechanism and Machine Science, therefore affiliated with IFToMM as its official research journal.
The main topics are:
Design Theory and Methodology;
Haptics and Human-Machine-Interfaces;
Robotics, Mechatronics and Micro-Machines;
Mechanisms, Mechanical Transmissions and Machines;
Kinematics, Dynamics, and Control of Mechanical Systems;
Applications to Bioengineering and Molecular Chemistry