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利用主成分分析方法，对TE模型产生的故障数据故障1进行故障检测-Using principal component analysis, on the TE model failure data generated by a fault detection fault
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基于PCA的故障检测 采用主元分析法 可以对其进行故障检测 生成ABC图-Based on PCA fault detection using principal component analysis method can generate the fault detection ABC figure
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核主元分析模型，用于故障检测，输入建模数据和待检测数据，计算T2和SPE统计量-Kernel Principal Component Analysis model for fault detection, input data for modeling and data to be detected, calculated T2 and SPE statistics
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实现故障检测的核主元分析方法,自己编的程序，稍微修改一下运行效果很好-Achieve fault detection kernel principal component analysis method
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利用主成分分析方法pca对数据进行降维处理和故障检测-Pca using principal component analysis for data dimensionality reduction and fault detection
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matlab程序，利用主元分析、T2统计图和贡献率图方法对数据进行故障检测和诊断，贡献率图是变量对失控得分进行贡献率计算-matlab program, using principal component analysis, T2 charts and maps of the contribution rate of the data for fault detection and diagnosis, the contribution rate of the variable on the m
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利用主元分析、T2统计图和贡献率图方法对TE仿真故障1数据进行故障检测和诊断，贡献率图是变量对超出T2控制限的失控得分进行贡献率计算-matlab program, using principal component analysis, T2 charts and maps of the contribution rate of a fault on the TE simulation data for fault detection and diagnosis, the contributio
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The key concept in principal component analysis (PCA) is to reduce a high dimensional data volume into a lower dimensional space, where the low dimensional data con-tams most of the useful information/variance contained in the original data set. The
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Principal Component Analysis (PCA) for fault detection and use T^2 statistic and SPE statistic
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基于主元分析的异常检测和故障诊断，用于对具有高度线性相关的测量数据进行分析和处理，其最终实现高维空间降维的目的。-Anomaly detection based on principal component analysis and fault diagnosis, used for highly linear correlation measurement data analysis and processing, its ultimate achieve the goal of higher
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PCA（主成分分析 principle component analysis），可用于二分类，故障检测等-PCA (principal component analysis ), can be used for two-class, fault detection, etc.
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PenSim Data
Simulated data for training set for Partial Least Square (PLS) or Principal Component Analysis (PCA) Fault Detection
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基于核主元分析（KPCA）的工业过程故障检测，代码已优化，运行效率高，有详细的注释，附有训练数据和测试数据。(Achieves fault detection of industrial processes based on Kernel Principal Component Analysis (KPCA); the code has been optimized for high operational efficiency; detailed notes are attached with
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