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基于混合威布尔(Weibull)分布的油纸绝缘可靠性分析及剩余寿命预测

Reliability Analysis and Residual Life Prediction of Oil-paper Insulation Based on Mixed Weibull Distribution
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摘要 牵引变压器是动车组重要能量转换部件,其中,油纸绝缘性能关系着牵引变压器的正常工作运行。为反映油纸绝缘退化的电气特征参量及化学特征参量,建立混合威布尔分布模型,对油纸绝缘进行可靠性分析及剩余寿命预测。首先,将击穿电压及抗拉强度作为特征参量分别进行威布尔分布参数估计,采用遗传算法对参数进行精度优化;采用熵权法分别得到特征参量的权重,最终得到油纸绝缘的可靠度函数、分布函数及概率密度函数,进一步得到油纸绝缘剩余寿命。结果表明:利用混合威布尔分布模型得到的可靠度曲线、分布曲线及概率密度曲线介于单一模型之间,其预测油纸绝缘剩余寿命为186.71 d。 Traction transformer is an important energy conversion component of EMU train.The oilpaper insulation performance is related to the normal operation of traction transformer.In order to comprehensively consider the electrical characteristic parameters and chemical characteristic parameters reflecting the degradation of oil-paper insulation,a hybrid Weibull distribution model is established to analyze the reliability and predict the residual life of oil-paper insulation.Firstly,the breakdown voltage and tensile strength are used as characteristic parameters to estimate the Weibull distribution parameters,and the genetic algorithm is used to optimize the accuracy of the parameters.The entropy weight method is used to obtain the weight of the characteristic parameters.Finally,the reliability function,distribution function and probability density function of the oil-paper insulation are obtained,and the residual life of the oil-paper insulation is further obtained.The results show that the reliability curve,distribution curve and probability density curve obtained by considering the mixed Weibull distribution model are between the single model,and the predicted residual life of oil-paper insulation is 186.71 d.
作者 牛重雅 张慧娟 NIU Chongya;ZHANG Huijuan(School of Mechanical and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou Gansu 730070,China;Mechatronics T&R Institute,Lanzhou Jiaotong University,Lanzhou Gansu 730070,China)
出处 《高速铁路新材料》 2024年第4期16-21,共6页 Advanced Materials of High Speed Railway
关键词 牵引变压器 油纸绝缘 混合威布尔分布 可靠性分析 剩余寿命预测 traction transformer oil-paper insulation mixed weibull distribution reliability analysis residual life prediction
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