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Remaining storage life prediction for an electromagnetic relay by a particle filtering-based method

机译:基于粒子滤波的电磁继电器剩余存储寿命预测

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In this paper, we propose a particle filtering-based method for predicting the remaining storage life (RSL) of electromagnetic relays. The RSL prediction problem here addressed has the following three distinctive features: i) limited measurement data available; ii) incomplete run-to-failure data; and iii) no model available for the physical degradation process. Then, to develop the method for RSL prediction, storage testing and degradation mechanism analysis have been carried out to obtain the knowledge and information needed to develop the physical model that supports the RSL prediction procedure. We discuss the three main steps of the proposed prediction method: parameter estimation, model validation and RSL prediction. Data from nine relays are used for estimating the initial parameter values distribution and data from one relay are used for RSL prediction. The RSL prediction results are compared with those obtained by a nonlinear curve-fitting method and a basic particle filtering algorithm. The comparison shows that the proposed method is more effective in predicting the RSL than the other methods. (C) 2017 Published by Elsevier Ltd.
机译:在本文中,我们提出了一种基于粒子滤波的方法来预测电磁继电器的剩余存储寿命(RSL)。这里解决的RSL预测问题具有以下三个鲜明特征:i)可用的有限测量数据; ii)不完整的运行失败数据; iii)没有适用于物理降解过程的模型。然后,为了开发用于RSL预测的方法,已经进行了存储测试和降级机制分析,以获得开发支持RSL预测过程的物理模型所需的知识和信息。我们讨论了所提出的预测方法的三个主要步骤:参数估计,模型验证和RSL预测。来自九个继电器的数据用于估计初始参数值分布,来自一个继电器的数据用于RSL预测。将RSL预测结果与通过非线性曲线拟合方法和基本粒子滤波算法获得的结果进行比较。比较表明,所提出的方法在预测RSL方面比其他方法更有效。 (C)2017由Elsevier Ltd.发布

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