...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Naval Research Logistics >Health assessment and prognostics based on higher-order hidden semi-Markov models
【24h】

Health assessment and prognostics based on higher-order hidden semi-Markov models

机译：基于高阶隐藏半马尔可夫模型的健康评估和预测

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

摘要

This paper presents a new and flexible prognostics framework based on a higher-order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective Gibbs sampling algorithm is designed for statistical inference of the HOHSMM. We conduct a simulation study to evaluate the performance of the proposed HOHSMM sampler and examine the impacts of the distant-history dependency. We design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life is predicted using a simulation approach given the decoded hidden states. The practical utility of the proposed prognostics framework is demonstrated by a case study on National Aeronautics and Space Administration (NASA) turbofan engines. We further compare the RUL prediction performance between the proposed HOHSMM and a benchmark mixture of Gaussians HMM prognostics method. The results show that the HOHSMM-based prognostics framework provides good hidden health-state assessment and RUL estimation for complex systems.
机译：本文介绍了基于高阶隐藏的半马尔可夫模型（HOHSMM）的新的和灵活的预测框架，用于具有不可观察的健康状态和复杂的转换动态的系统或组件。 HOHSMM通过允许隐藏状态缩短其更远的历史并假设通常分布式状态持续时间来扩展基本隐藏的Markov模型（HMM）。有效的GIBBS采样算法专为HOHSMM的统计推断而设计。我们进行仿真研究，以评估拟议的Hohsmm采样器的性能，并检查遥远历史依赖的影响。我们设计一种解码算法，可以使用学习模型来估计隐藏的健康状态。使用仿真方法给出剩余的使用寿命，给定解码的隐藏状态。拟议的预测框架的实用效用是通过国家航空航天局（NASA）TUBOOMAN发动机的案例研究。我们进一步比较了拟议的Hohsmm与高斯HMM预后方法的基准混合物之间的RUL预测性能。结果表明，基于HOHSMM的预测框架为复杂系统提供了良好的隐藏健康状态评估和RUL估计。

著录项

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