高速列车牵引电机轴承故障机理及智能诊断方法研究

项目名称： 高速列车牵引电机轴承故障机理及智能诊断方法研究

项目编号： No.51475065

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 机械、仪表工业

项目作者： 邓武

作者单位： 大连交通大学

项目金额： 84万元

中文摘要： 轴承作为高速列车牵引电机的关键部件，受到承重、传递、冲击等载荷联合影响，存在横移、沉浮、伸缩、旋转等自由度运动和复杂的随机振动固有特殊性，使其极易发生损坏，导致机破、停运等事故多发。本项目围绕高速列车牵引电机轴承系统，采用理论分析、数值计算和实验相结合，开展特殊服役环境下轴承故障动态演化机理、特征提取、智能诊断与实验研究。通过分析故障模拟与振动、噪声、温度、转频等多因素影响，研究基于动力学仿真模型的故障建模方法，探索故障信号传递规律，阐明故障动态演化机理，揭示轴承参数与故障状态之间的映射关系；从抑制和利用噪声出发，引入仿生模式识别、自适应随机共振理论和数学形态变换等方法，研究复杂路径下多源微弱故障信号的预处理、增强与特征提取方法，实现特征矩阵降维，提出多层融合的智能诊断方法和故障特征数据驱动的自适应趋势预测方法，为高速列车牵引电机健康监测与预警提供理论基础与方法支撑。

中文关键词： 牵引电机轴承；故障动态演化机理；信号传递模型；故障特征提取；智能诊断

英文摘要： Bearing is the crucial equipment of traction motor of China Railways High-speed(CRH) train, which is subjected to the joint effects of much more loads, there exist some natural particularitis of the freedom movements (traversing, ups and downs, stretching and rotating)and complex random vibration to easily damage and cause accidents of the broken machine and outage and so on.Traction motor bearing of CRH is selected as research object in this project. The fault dynamic evolution mechanism, feature extraction,intelligence diagnosis and experiment are researched by combinating methods of theoretical analysis,numerical computation and physical tests under the special service environments. By analyzing multiple factors of fault simulation,vibration,noise,temperature and frequency,bearing fault modeling method based on dynamics simulation model and transferring characteristics of fault signals are studied, and the fault dynamic evolution mechanism is clarified and mapping relations between bearing parameters and fault features are indicated. From the point of view of suppressing and utilizing noise,these methods of biomimetic pattern recognition,adaptive stochastic resonance theory and mathematical morphology transform are introduced to preprocess, strengthen and extract features of multi-source weak fault signals under complex route.The dimension reduction reconstruction of feature matrix is also studied in here. Hybrid intelligent diagnosis mtethod with multi-layered fusion is propoded by optimizing hierarchical structure and an adaptive trend prediction mothod based on fault features and data-driven is also propoded in this research. The goal is to provide the theoretical basis and method support for autonomously monitoring and forecasting the health status of traction motor of CRH train.

英文关键词： Traction Motor Bearing;Fault Dynamic Evolution Mechanism;Signal Transferring Model;Fault Feature Extraction;Intelligence Diagnosis