Research on health assessment based on Hidden Markov Model

机译：基于隐马尔可夫模型的健康评估研究

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The health assessment is an important part of Prognostic and Health Management. Since the early fault signals are difficult to detect, we argue to convert them into the information that easily observed by using Hidden Markov Model (HMM), evaluate current state that deviation from the normal state and estimate the health status for the maintenance decision of Condition Based Maintenance. In this paper, we describe the basic theory, discuss the implementation methods of HMM in detail, and give an example for validation. Experimental results show that the method can effectively assess the health status of equipment.

机译：健康评估是预后和健康管理的重要组成部分。由于早期故障信号难以检测，我们争辩于将它们转换为通过使用隐马尔可夫模型（HMM）轻松观察的信息，从而评估偏离正常状态并估计条件维护决策的健康状态的当前状态基于维护。在本文中，我们描述了基本理论，详细讨论了HMM的实现方法，并举例说明验证。实验结果表明，该方法可有效评估设备的健康状况。

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《International Conference on Electronic and Mechanical Engineering and Information Technology》|2011年||共3页
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Wen-hai Li; Yi-ping Wang; Yong-shuang Shang; De-qiang Yin;
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中图分类 TP39-53;
关键词
DC power; Hidden Markov Model; Prognostic and Health Management; health assessment; maintenance decision;

机译：直流电源;隐藏马尔可夫模型;预后和健康管理;健康评估;维护决定;

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Research on health assessment based on Hidden Markov Model

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