The principal component analysis (PCA) approach for sensor fault detection, identification and reconstruction in HVAC system is presented.
提出了空调系统传感器故障检测、故障识别、故障重构的主成分分析方法。
This thesis presents principal component analysis (PCA) approach for sensor fault detection, identification and reconstruction in HVAC system.
本文提出了用主成分分析法(PCA)进行空调系统传感器故障检测与诊断的基本思想。
Computer program is written via VB and MATLAB and the recognize system of tanker car is accomplished based on PCA characteristic pick-up and BP nerve network arithmetic.
应用VB和MATLAB编写了计算机程序，形成了基于罐车PC A特征和BP神经网络算法的识别系统。
This system can complete the function of picture pretreatment, PCA characteristic pick-up and BP nerve network training as well as recognize and so on.
该系统能够完成罐车图像的预处理、PCA特征提取、BP神经网络训练以及基于BP神经网络的识别等功能。
The system includes configuration environment and operation environment. The PCA model is built and verified and the statistic variable control limits are determined in the configuration environment.
该系统包括组态环境与运行环境，其中组态环境完成主元模型的建立与检验、统计量控制限的确定以及统计量监视图的组态等功能；
The PCA method is mainly used to research on fault detection while FDA to fault isolation and identification. The two methods interact and make up a complete fault diagnosis system.
其中着重使用PC A方法进行故障检测的研究，使用FDA方法进行故障分离和识别的研究，并将两种方法相互配合，构成完整的故障诊断系统。
Based on the analysis and establishment of a reasonable customer loyalty evaluation system, a combined PCA/DEA customer loyalty evaluation model was proposed.
在分析和构建一套合理的企业客户忠诚度评价指标体系的基础上，提出了一种基于PCA/DEA的企业客户忠诚度复合评价模型。
It can eliminate the correlation between variables and disturbance of system and can simplify complicated degree by PCA when reserving enough features of original data information.
利用主元分析法，可以在保留原有数据信息特征的基础上，消除变量关联和部分系统干扰，简化分析的复杂程度。
Finally we propose statistical classification based object detecting method, and realize the PCA based object detecting system. The system is experimented and achieves satisfactory results.
最后提出了基于统计分类的目标检测方法，并实现了基于主成分分析的目标识别系统，实验取得了比较理想的结果。
Secondly, the developing method of fault detection and diagnosis system based on PCA for the production process is discussed.
其次，着重研究了基于主元分析的生产过程异常检测与诊断系方法。
The EU provides Ukraine with a Generalized System of Preferences (GSP) as well as a Partnership and Co-operation Agreement (PCA) that took effect in 1998.
除了欧盟提供一般优惠待遇外，双边的合作夥伴协定亦于一九九八年生效。
The EU provides Ukraine with a Generalized System of Preferences (GSP) as well as a Partnership and Co-operation Agreement (PCA) that took effect in 1998.
除了欧盟提供一般优惠待遇外，双边的合作夥伴协定亦于一九九八年生效。
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