Improved Performance of Fault Detection Based on Selection of the Optimal Number of Principal Components
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摘要: 研究了一种基于主元分析故障检测确定主元数的新方法. 提出了信噪比并基于信噪比确定最优主元数. 通过最大化信噪比最优主元数被选择, 使故障检测性能得到改进. 这种方法被应用到TE过程中并与累积方差贡献率方法进行比较, 结果显示了此方法的优越性.Abstract: This paper presents a new method for selecting the number of principal components (PCs) in fault detection based on principal component analysis (PCA). On the basis of the proposed fault signal-to-noise ratio (SNR), the optimal number of PCs can be determined. SNR indicates the relationship between the sensitivity of fault detection and the number of PCs. By maximizing the fault SNR, the optimal number of PCs can be selected and the performance of fault detection can be improved. This method is applied to Tennessee Eastman process (TEP) and compared with the cumulative percent variance (CPV) method. The simulation results demonstrate its good Performance.
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