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。
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。
A classifiers ensemble approach based on Principal Component Analysis (PCA) was proposed.
设计了一种基于主成分分析的分类器集成方法。
Then it is analyzed that the pertinence of nest site selection of magpie and urban green space using principal component analysis and singe factor analysis approach.
通过主成分分析和单因子分析的方法分析喜鹊营巢特征与城市绿地环境的相关性。
Methods The methods of analysis were used by expert consulting graded approach, discrete tendency, principal component analysis and cluster analysis.
方法采用专家咨询打分法、离散趋势法、主成分分析法和聚类分析法进行分析。
An approach to gear fault diagnosis is presented, which bases on kernel principal component analysis (KPCA).
提出了基于核函数主元分析的齿轮故障诊断方法。
An approach to gear fault diagnosis is presented, which bases on kernel principal component analysis (KPCA).
提出了基于核函数主元分析的齿轮故障诊断方法。
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