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Optimization of Fault Detection/Diagnosis Model for Thermal Storage System Using AIC

机译：基于AIC的储热系统故障检测/诊断模型的优化

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摘要

The authors simulated the human ability of pattern recognition mathematically, through finding the state-change characteristics of the objective system from actual measurements using statistical analytical tech-niques. The Fourier analysis method with the space in-variance that can extract the changing characteristics of the system state, such as phase and frequency, is chosen as a typical technique. The distinction function based on Maharanobis' pan-distance is used to identify and judge the essence of the event. In addition, human learning, recognition, and optimal judgment process of any event can be simulated by optimizing the most effective pa-rameters and their numbers for detection and diagnosis by the use of variable selection method.In previous papers by authors [2], the two optimization methods for the most effective detection and diagnosis vector, the variable selection method and the differentia-tion rate increment method, in which linear distinction function based on Maharanobis' pan-distance was used, have been reported. In the present paper, a new method for optimal model selection based on AIC(Akaike In-formation Criteria) is examined. In addition, by exam-ining the influence to the distinction and diagnosis rate of the optimal detection and diagnosis vector, the best convergence criteria of AIC was confirmed.
机译：作者通过使用统计分析技术从实际测量中发现目标系统的状态变化特征，以数学方式模拟了人类的模式识别能力。典型的技术是选择具有空间不变性的傅里叶分析方法，该方法可以提取系统状态的变化特性，例如相位和频率。基于Maharanobis泛距的区分函数用于识别和判断事件的实质。另外，通过使用变量选择方法优化最有效的参数及其数量以进行检测和诊断，可以模拟任何事件的人类学习，识别和最佳判断过程。在以前的论文[2]中，已经报告了最有效的检测和诊断向量的两种优化方法，即变量选择法和微分率增加法，其中使用了基于Maharanobis泛距的线性区分函数。本文研究了一种基于AIC（Akaike信息准则）的最优模型选择方法。另外，通过考察最佳检测和诊断载体对区分率和诊断率的影响，确定了最佳的AIC收敛准则。

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    Pan S.; Zheng M.; Nakahara N.;

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  • 年度 2006
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  • 正文语种 en_US
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