• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Reliability >Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction
【24h】

Sensory-Based Failure Threshold Estimation for Remaining Useful Life Prediction

机译：剩余使用寿命预测的基于感官的故障阈值估计

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

The rapid development of sensor and computing technology has created an unprecedented opportunity for condition monitoring and prognostic analysis in various manufacturing and healthcare industries. With the massive amount of sensor information available, important research efforts have been made in modeling the degradation signals of a unit and estimating its remaining useful life distribution. In particular, a unit is often considered to have failed when its degradation signal crosses a predefined failure threshold, which is assumed to be known a priori. Unfortunately, such a simplified assumption may not be valid in many applications given the stochastic nature of the underlying degradation mechanism. While there are some extended studies considering the variability in the estimated failure threshold via data-driven approaches, they focus on the failure threshold distribution of the population instead of that of an individual unit. Currently, the existing literature still lacks an effective approach to accurately estimate the failure threshold distribution of an operating unit based on its in-situ sensory data during condition monitoring. To fill this literature gap, this paper develops a convex quadratic formulation that combines the information from the degradation profiles of historical units and the in-situ sensory data from an operating unit to online estimate the failure threshold of this particular unit in the field. With a more accurate estimation of the failure threshold of the operating unit in real time, a better remaining useful life prediction is expected. Simulations as well as a case study involving a degradation dataset of aircraft turbine engines were used to numerically evaluate and compare the performance of the proposed methodology with the existing literature in the context of failure threshold estimation and remaining useful life prediction.
机译：传感器和计算技术的飞速发展为各种制造业和医疗保健行业的状况监测和预后分析创造了前所未有的机会。由于有大量可用的传感器信息，因此在对单元的降级信号进行建模并估算其剩余使用寿命时已经做出了重要的研究工作。特别地，当单元的降级信号超过预定义的故障阈值时，通常认为该单元已发生故障，假定该阈值是先验的。不幸的是，考虑到潜在的降级机制的随机性，这种简化的假设在许多应用中可能无效。尽管有一些扩展的研究考虑了通过数据驱动方法估算的故障阈值的可变性，但它们侧重于总体的故障阈值分布，而不是单个单元的故障阈值分布。当前，现有文献仍然缺乏有效的方法来基于状态监测期间的原位感测数据来准确估计操作单元的故障阈值分布。为了填补这一文献空白，本文提出了一种凸二次方公式，该公式结合了历史单位的退化曲线信息和操作单位的现场感官数据，可以在线估计该特​​定单位在现场的故障阈值。通过实时更准确地估算操作单元的故障阈值，可以预期得到更好的剩余使用寿命预测。仿真和案例研究涉及飞机涡轮发动机的退化数据集，用于在故障阈值估计和剩余使用寿命预测的背景下，对所提出方法的性能进行数值评估，并将其与现有文献进行比较。

著录项

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