Research review on dynamics and fault diagnosis of railway vehicle gearboxes
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摘要: 从内外激励作用和故障状态下的动力学研究两方面综述了铁道车辆齿轮箱动力学建模、响应特性分析的研究进展,总结了不同模型在研究对象、考虑因素以及耦合复杂度上的优缺点,归纳了不同故障类型对齿轮箱动态特性的影响;系统阐释了信号处理方法和人工智能技术在铁道车辆齿轮箱故障诊断中的应用,探讨了现有方法的特点与不足之处。结果表明:铁道车辆齿轮箱的动力学建模和特性研究应充分考虑其特有的运行环境与结构特征,重点发展高精度的刚柔耦合多体动力学模型,并加强多物理场耦合建模与试验验证,为故障特征提取和健康状态评估提供更可靠的理论支持;加强对典型故障模式的故障演化机理研究,并结合混合建模、数字孪生等方法,开展复合故障场景模拟与全寿命仿真分析,为铁道车辆齿轮箱的故障预测与健康管理提供理论依据;开发更先进的故障诊断技术,并结合真实列车数据验证诊断方法的有效性,以满足铁道车辆故障诊断的高精度和实时性要求,推动铁道车辆齿轮箱全寿命周期健康管理与运维决策的理论和实践进展。Abstract: The research progress on dynamic modeling and response characteristics analysis of railway vehicle gearboxes was reviewed from two aspects: the dynamic research under internal and external excitation, and the dynamic research under fault conditions. The advantages and disadvantages of different models were summarized in terms of the research object, consideration factors, and coupling complexity. The influence of different fault types on the dynamic characteristics of gearboxes was concluded. The application of signal processing methods and artificial intelligence technologies in the fault diagnosis of railway vehicle gearbox was systematically elaborated. The characteristics and shortcomings of the existing methods were also discussed. The results show that the dynamic modeling and characteristic research of railway vehicle gearboxes should fully consider the unique working environment and structural characteristics of gearboxes, focus on developing high-precision rigid-flexible coupling multi-body dynamic models, and strengthen the modeling and experimental verification of multi-physics field coupling, thus providing more reliable theoretical support for fault feature extraction and health status assessment. In addition, the research on the fault evolution mechanism of typical fault modes should be enhanced. The methods including hybrid modeling and digital twin should be combined to conduct composite fault scenario simulation and full-life simulation analysis, offering a theoretical basis for the fault prediction and health management of railway vehicle gearboxes. Finally, more advanced fault diagnosis technologies should be developed with actual train data to verify the effectiveness of the diagnostic methods, so as to meet the high precision and real-time requirements of railway vehicle fault diagnosis, and promote the theoretical and practical progress of health management and operation decision-making throughout the entire life cycle of railway vehicle gearboxes.
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Key words:
- railway vehicle /
- gearbox /
- review /
- fault diagnosis /
- dynamics /
- signal processing /
- artificial intelligence
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表 1 铁道车辆齿轮箱动力学研究综述
Table 1. Summary of dynamics studies on railway vehicle gearboxes
研究主题 研究方法 研究模型 参考文献 振动特性 LPM-FEM 模型A [12]、[14] 模型b [15] 模型C [22]、[27]、[32]、[36]、[37]、[42]、[46]、[47] 模型D [43] LPM-TM、AM-TM 模型B [13] 模型C [23]、[29] TM [18]~[21]、[26]、[45] LPM-FEM-TM 模型B [16]、[24] LPM、AM 模型a [25] 模型C [28] 模型D [30]、[44] 应力分析 LPM-FEM 模型C [31]、[34] FEM-TM 模型A [33] TM [20]、[35]、[74] 混沌特性 LPM 模型B [17] 模型b [38]、[39] 机电耦合振动 机电联合仿真 模型c [40]、[41] 故障特性 LPM、AM 模型b [57] 模型B [58]、[75] 模型a [59]~[61]、[63]、[74] FEM 高速列车齿轮副有限元模型 [62] 模型B [72] TM [67]、[70]、[71] LPM-FEM 模型A [69] 模型C [76]、[77] 表 2 现有故障机理研究综述
Table 2. Summary of existing fault mechanism studies
故障类型 参考文献 年份 总结 轮齿裂纹 [57] 2020 当齿轮发生齿裂时,齿轮系统的振动幅值增大。转子系统的振动能量分布向较低的共振频带偏移,齿轮啮合频带出现明显的调制现象。动态啮合力、轮对纵向和垂向振动加速度的时频分析结果可以反映出齿裂故障特征频率 [58] 2022 [59] 2018 [60]、[61] 2020、2018 [63] 2019 [64] 2023 [62] 2017 壳体裂纹 [67] 2006 由于轨道激励等的影响,齿轮箱发生局部共振,导致壳体特定区域的动应力幅值增大,从而导致裂纹。在车轮多边形磨损的影响下,齿轮箱壳体的使用寿命明显降低 [69] 2019 [70] 2017 [71] 2018 [72] 2018 [73] 2024 齿轮剥落 [74] 2023 从齿轮箱壳体振动的时频谱来看,齿轮剥落可能不会表现出明显的侧带。为了准确提取故障特征频率,需要有效的去噪和弱特征提取算法 齿轮磨损 [75] 2019 从齿轮振动频谱来看,齿轮故障频率主要集中在齿轮啮合频带。在磨损失效的初始阶段,磨损对振动信号的均方根值、峭度和峰-峰值影响最大 [58] 2022 齿轮偏心 [76] 2021 齿轮偏心增大了动态啮合力和轮轨纵向蠕滑力的幅值 轴承滚道剥离故障 [77] 2020 引起高频振动,导致齿轮箱内共振,从而增加齿轮啮合部件的振动幅度。轴承出现故障时,相比于齿轮故障,传动系统的振动能量大多分布在更高频段 [78] 2020 [58] 2022 [79] 2025 轴承滚动体故障 [80] 2025 滚动体局部缺陷与车轮多边形的阶次激励共同作用下,高阶激励会显著增强振动响应并引起复杂频谱变化,存在显著耦合效应 表 3 铁道车辆故障诊断研究总结
Table 3. Summary of fault diagnosis studies for railway vehicles
技术类别 参考文献 关键技术 信号类型 信号处理 [52] 互补集成经验模态分解 振动 [82] 集合经验模态分解、希尔伯特变换 振动 [83] 变尺度经验模态分解 振动 [84] 经验模态分解、奇异值差分谱理论 振动 [85] 改进谱峭度、自适应可调品质因子小波变换 振动 [86] 双树复小波变换、全变分算法 振动 [87] 最大相关峭度反卷积、天牛群优化算法 振动 [88] 奇异值分解、多点优化最小熵解卷积修正 振动 [89] 最小熵解卷积、参数优化变分模态分解 振动 [90] 最大相关峰度反卷积、Morlet复小波滤波、小波包奇异谱熵、学习向量量化算法 振动 [93] 小波能量熵、双树复小波变换 电流 [94] 小波双谱、小波双谱熵 电流 [95] 维纳状态退化过程、多传感器滤波 温度 [96] 变分模态分解、局部均值分解 振动、温度 [97] 时间序列独立成分分析 温度、电流 传统机器学习 [98] 信念规则库、DS证据理论 温度 [99] 深度慢特征分析、信念规则库 振动、温度 [100] 经验模态分解、局部线性嵌入、支持向量机 振动 [59] 主成分分析、灰色关联分析 振动 [101] AdaBoost算法 三维CT数据 [102] 加权支持向量机 声发射 [103] 双目立体视觉技术 点云数据 [104] 海灵格距离、慢特征分析、隐马尔可夫模型 温度 [105] 慢特征分析 温度、振动 [106] 自学习、模糊聚类算法 振动 [107] 可拓理论 振动 [108] 集合经验模态分解、自回归、支持向量回归 振动 深度学习 [109] 错位时频表示、卷积神经网络 振动 [110] 支持向量机、长短期记忆网络 温度 [111] 深度卷积网络、多通道信息融合、迁移学习 声音 -
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