【作者】 刘伟；

【导师】 杨江天；

【作者基本信息】 北京交通大学 ， 机械工程（专业学位）， 2021， 硕士

【摘要】 滚动轴承剩余使用寿命预测是基于状态维修的重要一环。电机定子电流中蕴藏着反映电机工作状况的丰富信息。针对HXN3型机车辅助电机轴承使用寿命预测的需求,本文将电机定子电流分析方法引入故障预测,从电机定子电流中提取各诊断指标,用新陈代谢灰色-粒子滤波组合预测模型定量描述机械故障的发展,取得成果如下:(1)电机定子电流相当于检测电机故障的传感器,轴承故障会使电机电流频谱中映射出新的频率成分。根据这一特性,从电流信号中提取出对频谱变化敏感的特征参数,能描述轴承故障随时间的发展。论文研究成功地从电机电流信号中提取了反映电机轴承状态的三种特征参数:小波包能量熵、功率谱熵和频率标准差。实验证明,三种参数的变化趋势均随故障程度的增加而单调递增。(2)为了准确预测轴承故障,本文提出了新陈代谢灰色-粒子滤波组合预测模型。该方法利用新陈代谢灰色模型建立动态空间状态模型,然后将模型带入粒子滤波算法中,建立组合预测模型。该方法发挥两种模型各自的优势,描述轴承故障随时间的发展。(3)工业现场采集多台HXN3型机车辅助电机定子电流信号,提取小波包能量熵、功率谱熵和频率标准差3个诊断指标,建立新陈代谢灰色-粒子滤波组合预测模型,预测3个参数随时间的发展。实验结果表明,本文提出的电机轴承故障预测方法,考虑多个诊断指标,非常适合量化故障随时间的发展和确定轴承剩余使用寿命。更多还原

【Abstract】 Bearing fault prognosis and remaining useful life prediction is a key problem in condition based maintenance.The motor stator current contains abundant information which reflects the working condition of motors.In the light of the remaining useful life prediction of auxiliary motor bearings of the HXN3 type locomotive,an efficient fault forecasting procedure using motor current signature and combined prediction model is presented.In this procedure,the condition monitoring is performed based on the on-line motor current measurements,and further,the fault quantification is performed by the grey renewal-particle filter forecasting model.The contributions are made as follows:Stator current acts as an excellent transducer for detecting faults in motors.The bearing faults induce the relating frequency components in stator current,and then the stator current spectrum changes slightly.According to this this principle,the quantitative indices which are sensitive to the current spectrum can be used to describe the development of faults.Then,three quantitative indices,i.e.the spectral entropy,the wavelet packet energy entropy and the frequency standard deviation were extracted from the motor current signal.Experimental results show that the three parameters increase monotonically with motor bearing faults.For precise and reliable fault prediction,a combined prediction model,i.e.the grey renewal-particle filter forecasting model is proposed.In this procedure,a dynamic spatial state model is established first using the grey renewal model.Then,the model is brought into the particle filter algorithm to establish a combined prediction model.Since this forecasting procedure take advantages of two different models,the changes in each index can be described accurately.The stator current signals of the auxiliary motors of HXN3 type locomotives were sampled.The diagnostic indicators,such as the wavelet packet energy entropy,the spectral entropy and the frequency standard deviation were extracted and the proposed combined prediction models were established.The experimental results show that the proposed forecasting procedure considers several diagnostic,therefore it is highly suitable to quantify the faults development and to determine the bearing remaining useful life.更多还原