• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Reliability prediction based on complicated data and dynamic data.
【24h】

Reliability prediction based on complicated data and dynamic data.

机译：基于复杂数据和动态数据的可靠性预测。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Lifetime data from the field can be complicated due to truncation, censoring, multiple failure modes, and the nonhomogeneity of the population. These complications lead to difficulties in reliability predictions and calibrations of the prediction intervals (PIs). Another trends in field lifetime data is the availability of the dynamic data which give in- formation dynamically on how a product being used and under which environment being used. Incorporating this information (historically not available) into statistical analyses will provide stronger statistical methods. In this dissertation, statistical models and methods motivated by real applications were developed for reliability predictions based on complicated data and dynamic data. In Chapter 2, left truncated and right censored high-voltage power transformer lifetime data are available from an energy company. The company wants to predict the remaining life of transformers and the cumulative number of failures at a future time for their transformer fleet. The population is nonhomogeneous because transformer designs evolved over past decades. The data were stratified into relatively homogeneous groups and regression was done to incorporate the explanatory variables. The random weighted bootstrap was used to overcome the difficulties introduced by the complicated structure of the data in the calibration of the prediction intervals. In Chapter 3, the importance of stratification when the population is nonhomogeneous was analytically studied in the context of reliability predictions. There are two potential pitfalls for fitting a single distribution to nonhomogeneous data, which are misinterpretation of the failure mode and asymptotic biasness in prediction. These results were further illustrated by the high-voltage transformer life data. In Chapter 4, data are available from a product which has four major failure modes. Use-rate information is available for units connected to the network. We use a cycles-to-failure model to compute predictions and prediction intervals for the number failing. We also present prediction methods for units not connected to the network.
机译：由于截断，检查，多种故障模式以及总体的不均匀性，来自现场的终身数据可能会变得复杂。这些复杂性导致可靠性预测和预测间隔（PI）校准的困难。现场寿命数据的另一个趋势是动态数据的可用性，动态数据动态地提供了有关产品的使用方式以及在何种环境下使用的信息。将这些信息（历史上不可用）纳入统计分析将提供更强大的统计方法。本文针对复杂数据和动态数据，开发了基于实际应用的统计模型和方法，用于可靠性预测。在第2章中，可从一家能源公司获得左截断的和右删截的高压电力变压器的寿命数据。该公司希望预测他们的变压器机队的变压器剩余寿命和将来的累计故障数。人口是不均匀的，因为变压器设计是在过去数十年间发展起来的。将数据分为相对均匀的组，并进行回归以纳入解释变量。使用随机加权引导程序克服了预测间隔校准中数据结构复杂带来的困难。在第三章中，在可靠性预测的背景下，分析了人口不均匀时进行分层的重要性。将单个分布拟合到非均匀数据存在两个潜在的陷阱，这是对故障模式的误解和预测中的渐近偏差。高压变压器寿命数据进一步说明了这些结果。在第4章中，可从具有四种主要故障模式的产品中获取数据。使用率信息可用于连接到网络的设备。我们使用一个失效周期模型来计算失败次数的预测和预测间隔。我们还介绍了未连接到网络的设备的预测方法。

著录项

  • 作者

    Hong, Yili.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 129 p.
  • 总页数 129
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 统计学;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号