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基于Wiener过程的霍尔电流传感器可靠性预测

Hall Current Sensor Lifetime Prediction Based on the Wiener Process
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摘要 霍尔电流传感器的退化机理复杂,退化具有波动性,非线性等特征,对高可靠、长寿命的霍尔电流传感器准确进行寿命预测是一个难点。本研究通过对霍尔电流传感器进行加速退化试验,利用性能退化数据,预测了传感器的可靠性及寿命。首先分析了霍尔电流传感器工作原理和退化机理,确定将输出电流漂移作为其性能退化参数。然后由试验数据推导得到Wiener过程漂移参数和扩散参数的约束关系,结合阿伦尼乌斯模型推导得到漂移参数和扩散参数的加速模型。从而得到霍尔电流传感器在正常工作温度条件下的可靠度函数和可靠寿命。将结果与基于加速退化轨迹法的可靠性预测结果进行对比,验证了本方法的可行性。 The degradation mechanism of Hall current sensors is complex,and the degradation has the characteristics of volatility and nonlinearity,and it is a difficult point to accurately predict the life of Hall current sensors with high reliability and long life.In this study,the life prediction of Hall current sensors was studied under accelerated degradation experimental conditions.Firstly,the working principle and degradation mechanism of Hall current sensors are studied,so as to determine the output current drift as its performance degradation parameter.Then,the constraint relationship between Wiener process drift parameters and diffusion parameters is derived from the experimental data,and the acceleration model of drift parameters and diffusion parameters is derived by combining the Arrhenius model.The reliability function and reliable life of the Hall current sensor under normal operating temperature conditions are obtained.The feasibility of the proposed method is verified by comparing the results with the reliability prediction results based on the accelerated degradation trajectory method.
作者 肖保明 鞠文静 XIAO Bao-ming;JU Wen-jing(State Grid Electric Power Research Institute,State Key Laboratory of Smart Grid Protection and Control,Nanjing 211106)
出处 《环境技术》 2023年第5期59-63,69,共6页 Environmental Technology
基金 国家电网有限公司总部管理科技项目资助,项目编号:5700-202141450A-0-0-00。
关键词 加速退化试验 WIENER过程 阿伦尼乌斯模型 可靠性 accelerated degradation test wiener process arrhenius model reliability
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  • 1邓爱民,陈循,张春华,汪亚顺.基于性能退化数据的可靠性评估[J].宇航学报,2006,27(3):546-552. 被引量:133
  • 2鲁光辉.霍尔电流传感器的性能及应用[J].四川文理学院学报,2007,17(2):40-42. 被引量:41
  • 3Zehua,C.Shurong,Z.Lifetime distribution based degradation analysis[J].IEEE Transactions on Reliability,2005,54(1):3-10
  • 4Jayaram,J,S,R.,Girish,T.Reliability prediction through degradation data modeling using aquasi-likelihood approach[C].Proceedings Annual Reliability and Maintainability Symposium,2005:193-199
  • 5Di,X.,Wenbiao,Z.Reliability prediction using multivariate degradation data[C].Proceedings Annual Reliability and Maintainability Symposium,2005:337-341
  • 6Crk,V.Reliability assessment from degradation data[C].Proceedings Annual Reliability and Maitainability Symposium,2000:155-161
  • 7Lu,J.C.,Meeker,W.Q.Using degradation measures to estimation a time-to-failure distribution[J].Technometrics,1993,35(2):161-174
  • 8Lu,J.C.,Jinho Park,Qing Yang.Statistical inference of a time-tofailure distribution derived from linear degradation data[J].Technometrics,1997,39(4):391-400
  • 9Meeker,M.Q.,Escobar,L.A.Statistical Methods for Reliability Data[M].New York:John Wiley & Sons,Inc.1998
  • 10肖成勇,雷振山,魏丽.LabVIEW2010基础教程[M].北京:中国铁道出版社,2012.

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