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首页> 外文期刊>Insight >Magnetoacoustic fusion life prediction method for retired components based on D-S evidence theory
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Magnetoacoustic fusion life prediction method for retired components based on D-S evidence theory

机译：基于D-S证据理论的磁声融合寿命预测方法

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摘要

Prediction of the remaining life of remanufacturing blanks is crucial to evaluate their remanufacturability To overcome the deficiency of obtaining insufficient fatigue damage characteristic information using a single non-destructive testing method, a new magnetoacoustic fusion life prediction method based on Dempster-Shafer (D-S) evidence theory optimised by a weighted fusion algorithm is proposed. The characteristic parameters of metal magnetic memory (MMM) and acoustic emission (AE) signals are first extracted on the basis of a fatigue experiment and data layer fusion is carried out to establish the mapping relationship between MMM and AE characteristic parameters and specimen life based on a back-propagation (BP) neural network. The basic probability distribution of the life is assigned in a fuzzy manner according to the normal distribution and the reliability function of each life interval is obtained by data fusion based on D-S evidence theory. Furthermore, the basic probability distribution value is modified based on a weighted fusion algorithm and the corrected data are fused to obtain a more accurate life prediction result.
机译：再制造空白的剩余寿命的预测对于评估它们使用单​​一非破坏性测试方法克服其缺乏的缺陷来评估它们的再制造性，这是一种基于Dempster-Shafer（DS）证据的新的磁声融合寿命预测方法提出了由加权融合算法优化的理论。首先在疲劳实验的基础上提取金属磁存储器（MMM）和声发射（AE）信号的特征参数，并进行数据层融合，以建立MMM和AE特征参数与标本寿命之间的映射关系反向传播（BP）神经网络。根据正常分布以模糊方式分配寿命的基本概率分布，并且基于D-S证据理论，通过数据融合获得每个生命间隔的可靠性函数。此外，基于加权融合算法修改基本概率分布值，并且校正的数据被融合以获得更准确的寿命预测结果。

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  • 来源
    《Insight》 |2021年第8期|488-495|共8页
  • 作者

    YT Gao; Z M Hu; J C Leng;

  • 作者单位

    Northeast Petroleum University Daqing 163318 PR China;

    No 1 Geo-Logging Company of CNPC Daqing Drilling & Exploration Engineering Corporation Daqing 163411 PR China;

    Northeast Petroleum University Daqing 163318 PR China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    magnetoacoustic fusion; BP neural network; D-S evidence theory; weighted fusion algorithm;

    机译：磁音融合;BP神经网络;D-S证据理论;加权融合算法;

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