...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >Dynamic Bayesian monitoring and detection for partially observable machines under multivariate observations
【24h】

Dynamic Bayesian monitoring and detection for partially observable machines under multivariate observations

机译：多元观测下部分可观察机的动态贝叶斯监测和检测

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

Due to restrictions of mechanical structure and sensor installation, the actual status of modern engineered machine is unobservable and only partial observations can be indirectly collected. For this type of machine, existing research works used several measurable variables with fixed thresholds to monitor and detect the failures of the system, which leads to suboptimal results. This paper proposes a dynamic monitoring and detection approach for partially observed machines under discrete multivariate observations. The actual status of the partially observed machine system is modeled as a multivariate hidden semi-Markov process with unobservable operational states and an observable failure state under a general sojourn time structure. The process parameters are estimated using expectation maximization (EM) algorithm. Based on the dynamics of the hidden state of machine deterioration, a dynamic Bayesian control chart is formulated to monitor the posterior probability of system in warning state and adaptively switches the monitoring frequency according to risks of potential failures. As the posterior probability statistic exceeds a certain level, full inspection is initiated to detect the impending failures. The objective of the dynamic Bayesian control scheme is to achieve the maximum long-run system average availability, and the optimal control problem of monitoring and detection is formulated and solved by a computational algorithm in a semi-Markov decision process (SMDP) framework. The entire procedure is illustrated using multivariate data from case studies of mechanical generators and feed systems. Comparison with other advanced methods is also given, which demonstrates a considerably better performance of the proposed dynamic Bayesian approach.
机译：由于机械结构和传感器安装的限制，现代工程机的实际状态是不可观察的，只能间接收集部分观察。对于这种类型的机器，现有的研究工作使用了几种可测量的变量，具有固定阈值来监视和检测系统的故障，从而导致次优效果。本文提出了在离散多变量观测下部分观测的机器的动态监测和检测方法。部分观测的机器系统的实际状态被建模为多变量隐藏的半马尔可夫过程，其具有不可观察的运行状态和一般侦察时间结构下的可观察失败状态。使用期望最大化（EM）算法估计过程参数。基于机器劣化的隐藏状态的动态，配制动态贝叶斯控制图以监测警告状态下系统的后验概率，并根据潜在故障的风​​险自适应地切换监控频率。随着后验概率统计量超过一定水平，启动完整检查以检测即将发生的失败。动态贝叶斯控制方案的目的是实现最大的长期系统平均可用性，并且通过半马尔可夫决策过程（SMDP）框架中的计算算法制定和解决了监测和检测的最佳控制问题。使用来自机械发电机和进料系统的案例研究的多变量数据来说明整个过程。还给出了与其他先进方法的比较，这证明了提出的动态贝叶斯方法的显着性能。

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