高速列车转向架故障诊断智能决策方法研究

发布时间：2018-01-10 19:22

本文关键词：高速列车转向架故障诊断智能决策方法研究　出处：《西南交通大学》2017年硕士论文　论文类型：学位论文

【摘要】：在高速列车长期服役过程中,列车转向架关键部件的性能蜕化与故障对列车的安全运行造成严重威胁。列车运行过程中通过在转向架不同位置安装各种类型的传感器对转向架进行评估,对高速列车的安全运营有重要意义。本文通过对监测数据特征提取的分析,建立特征提取知识库,构建了故障诊断决策模型。由于支持向量机参数对其性能影响较大,通过改进的粒子群优化算法优化支持向量机参数。对列车转向架的原车、2种位置的空气弹簧故障、4种位置的横向减振器故障以及8种位置的抗蛇行减振器故障这15种工况,给出了基于分级策略的诊断框架。具体的研究工作如下:1、利用已有的多种列车转向架振动信号特征提取方法,建立高速列车转向架振动信号的特征提取知识库,构建了高速列车转向架故障诊断决策模型,并对诊断决策模型进行了数学描述。2、在故障诊断模型中,通过改进的粒子群优化算法选择支持向量机的惩罚因子以及核函数参数。针对粒子群优化算法易陷入局部最优的问题,给出改进方法,首先,速度更新公式乘以收缩因子,其次,惯性权重采用高斯函数递减策略。利用公开数据进行测试,结果表明使用改进后的粒子群优化算法对支持向量机参数优化能够提高分类的正确率。3、基于分级策略,给出了列车转向架故障分级诊断框架,并根据实际情况确立了分级策略下高速列车转向架故障诊断的分级顺序,诊断顺序依次为原车、空气弹簧失效、横向减振器失效与抗蛇行减振器失效。利用对振动信号离散傅里叶变换的幅值作为特征进行原车的识别;利用振动信号的奇异谱熵、功率谱熵、小波能谱熵和小波空间特征谱熵对空气弹簧失效、横向减振器失效和抗蛇行减振器失效进行故障识别;利用振动信号的奇异谱熵、功率谱熵、小波能谱熵和小波空间特征谱熵对空气弹簧失效进行故障定位。利用改进后的粒子群优化算法对支持向量机参数进行优化。结果表明分类结果的正确率较高,与已有的研究相比较正确率有较大提高。
[Abstract]:In the high-speed train during long term, pose a serious threat to performance degeneration and fault plane of train key components to safe operation of the train. The train running through the steering sensor aircraft of various types of installation in different positions to assess the bogie, is of great significance to the safe operation of high-speed train. This paper through the analysis on the extraction the characteristic of monitoring data, establish feature extraction knowledge base, constructs the decision model of fault diagnosis. Because the parameters of SVM has great influence on its performance, the machine to the volume by the improved particle swarm optimization algorithm to optimize the support parameters of train bogie. The original car, 2 position of the air spring fault, lateral damper fault 4 position and yaw damper fault 8 position of the 15 conditions, gives the diagnosis framework based on classifying strategy. The specific research work are as follows: 1, using the existing Frame vibration signal characteristic extraction method for multi train steering, the establishment of high-speed train steering characteristics of frame vibration signal extraction of knowledge base, the construction of high-speed train bogie decision model of fault diagnosis, and the diagnostic decision model and the mathematical description of the.2 in the model of fault diagnosis, the improved particle swarm optimization algorithm selection of support vector machine the penalty factor and kernel parameter. To solve the problem of particle swarm optimization algorithm is easy to fall into local optimum, the improvement method is given firstly, velocity updating formula multiplied by the contraction factor, secondly, the inertia weight decreasing strategy. Gauss function was tested by using public data, results show that using the improved particle swarm optimization algorithm for parameter optimization of support vector machine to improve the classification accuracy of.3, based on the hierarchical strategy, gives the train bogie hierarchical fault diagnosis framework, and according to the actual situation. The high-speed train bogie grading classification strategy under sequential fault diagnosis, the diagnosis is in the order of the original car, air spring failure, failure and yawdamper lateral damper. The amplitude of the vibration signal of the discrete Fourier transform as the feature of the original car identification; using the singular spectrum entropy of vibration signal and power spectrum entropy, wavelet energy spectrum entropy and wavelet space feature entropy on the failure of the air spring, failure fault recognition of lateral damper failure and anti hunting damper; vibration signal by using the singular spectrum entropy, power spectrum entropy, wavelet energy spectrum entropy and wavelet space feature entropy for fault location of air spring failure by particle. Swarm optimization algorithm is improved to optimize the parameters of support vector machine. The results show that the classification accuracy rate is higher, compared with the existing research on accuracy is greatly improved.

【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U279.323;TP18

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