基于加速退化试验的不同失效机制下电机地壁绝缘材料寿命预测

Shihu Xiang;Guiheng Li;Feng Zhou;Shaopo Huang;Zijun Xu;Guanlong Jia;Feng Niu
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引用次数: 0

摘要

电动机地壁绝缘老化击穿可能导致严重的故障和安全事故,因此对地壁绝缘材料的寿命进行预测具有重要意义。本文对一种广泛应用于电机的GW绝缘材料聚酰亚胺(PI)薄膜进行了高温加速降解试验,获得了加速应力下的绝缘电阻、绝缘电容、介电损耗角正切和最大局部放电四项关键绝缘指标。实验结果表明,由于不同温度应力下的破坏机制不同,单一模型无法准确模拟最大局部放电的演变过程。因此,提出了Wiener-inverse Gaussian (IG)融合模型来预测PI膜的最大局部放电,并结合Arrhenius模型预测不同温度下PI膜的寿命。进一步进行了实验验证,基于实验验证数据,采用Kolmogorov-Smirnov检验、赤池信息准则和面积比等方法从多个角度验证了融合模型的准确性。提出的Wiener-IG融合模型能较好地拟合不同失效机制下的退化数据,可用于预测GW绝缘材料的寿命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Life Prediction of Ground-Wall Insulation Material in Electric Motors Based on Accelerated Degradation Test under Different Failure Mechanisms
The aging and breakdown of ground-wall (GW) insulation in electric motors may lead to serious faults and safety accidents, so predicting the life of GW insulation materials is important. In this article, high temperature accelerated degradation tests are performed on polyimide (PI) film, a kind of GW insulation material widely used in electric motors, to obtain its four key insulation indexes under accelerated stress, including insulation resistance, insulation capacitance, tangent of dielectric loss angle and maximum partial discharge. Experiment results show that a single model cannot accurately simulate the evolution of maximum partial discharge, due to the different failure mechanisms under different temperature stresses. Therefore, a Wiener-inverse Gaussian (IG) fusion model is proposed to predict the maximum partial discharge of PI film, and together with the Arrhenius model, the life of PI film under different temperatures can be predicted. Further experimental verification has been performed, and the accuracy of the proposed fusion model is demonstrated from multiple perspectives by using the methods of Kolmogorov–Smirnov test, Akaike information criterion, and area ratio based on the data from experimental verification. The proposed Wiener-IG fusion model can better fit the degradation data under different failure mechanisms, and then can be used to predict the life of GW insulation material.
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