A review of dynamics-based failure modeling and diagnosis techniques for gear systems

IF 4.5 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Yang Liu , Zheng’ang Shan , Haiying Liang , Qingyang Sun , Hui Ma
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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.
基于动力学的齿轮系统故障建模与诊断技术综述
齿轮系统在工业和军事应用中至关重要,但在恶劣条件下容易发生故障,导致经济损失和安全风险。本文对齿轮系统基于动力学的故障建模和诊断技术进行了全面的综述。它系统地评估了三种建模方法:解析方法(高效但仅限于简单系统),集中参数模型(平衡效率和多自由度动力学)和有限元方法(高精度但计算量大)。强调了将这些方法与机器学习相结合的混合策略,以提高计算效率和准确性。分析了常见的失效机制,包括裂纹、点蚀和磨损,强调了它们对时变网格刚度(TVMS)和振动特性的影响。讨论了用于故障特征提取和诊断的信号处理和机器学习技术,其中变分模态分解和人工智能增强模型等先进方法展示了卓越的性能。指出了实时诊断、模型通用性和耦合故障分析方面的挑战。未来的发展方向是提出混合人工智能物理模型、数字孪生和多尺度框架,以改善预测性维护。本综述将理论见解与实际应用相结合,为提高齿轮系统可靠性和智能故障诊断提供基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mechanism and Machine Theory
Mechanism and Machine Theory 工程技术-工程:机械
CiteScore
9.90
自引率
23.10%
发文量
450
审稿时长
20 days
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
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