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A Well-Designed Parameter Estimation Method for Lifetime Prediction of Deteriorating Systems with Both Smooth Degradation and Abrupt Damage

机译:一种设计良好的参数估计方法,用于同时预测具有光滑退化和突变破坏的退化系统的寿命

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摘要

Deteriorating systems, which are subject to both continuous smooth degradation and additional abrupt damage due to a shock process, can be often encountered in engineering. Modeling the degradation evolution and predicting the lifetime of this kind of systems are both interesting and challenging in practice. In this paper, we model the degradation trajectory of the deteriorating system by a random coefficient regression (RCR) model with positive jumps, where the RCR part is used to model the continuous smooth degradation of the system and the jump part is used to characterize the abrupt damage due to random shocks. Based on a specified threshold level, the probability density function (PDF) and cumulative distribution function (CDF) of the lifetime can be derived analytically. The unknown parameters associated with the derived lifetime distributions can be estimated via a well-designed parameter estimation procedure on the basis of the available degradation recordings of the deteriorating systems. An illustrative example is finally provided to demonstrate the implementation and superiority of the newly proposed lifetime prediction method. The experimental results reveal that our proposed lifetime prediction method with the dedicated parameter estimation strategy can get more accurate lifetime predictions than the rival model in literature.
机译:在工程中经常会遇到不断恶化的系统,这些系统既要经受连续的平滑退化,又要承受因冲击过程引起的额外的突然损坏。在实践中,对退化演变进行建模并预测此类系统的寿命既有趣又充满挑战。在本文中,我们通过具有正跳跃的随机系数回归(RCR)模型对退化系统的退化轨迹进行建模,其中RCR部分用于建模系统的连续平稳退化,而跳跃部分用于表征系统的连续平滑退化。由于随机冲击而突然损坏。基于指定的阈值水平,可以通过分析得出寿命的概率密度函数(PDF)和累积分布函数(CDF)。可以基于性能下降的系统的可用降级记录,通过精心设计的参数估计程序来估计与导出的寿命分布关联的未知参数。最后提供一个说明性示例,以演示新提出的寿命预测方法的实现和优越性。实验结果表明,与文献中的竞争模型相比,我们提出的带有专用参数估计策略的寿命预测方法可以获得更准确的寿命预测。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第10期|753175.1-753175.9|共9页
  • 作者

    Yu Chuanqiang; Jiang Cheng;

  • 作者单位

    Hightech Inst Xian, Xian 710025, Shaanxi, Peoples R China.;

    Hightech Inst Xian, Xian 710025, Shaanxi, Peoples R China.;

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