Sparse principal component analysis (SPCA) has recently emerged as an approach aimed at producing compact principal component loadings by suppressing spurious values and thus overcoming some limitations of the traditional principal component analysis (PCA). This paper proposes a fault detection and diagnosis (FDD) method based on SPCA; in this approach, the number of non‐zero loadings (NNZL) of SPCAs is selected based on both the false alarm rate (FAR) and the fault detection rate (FDR). The criterion is to have lower FAR and higher FDR. This new feature makes SPCA better suited for FDD, which is demonstrated by comparing its performance with that of three other methods for finding loadings. The overall FDD performances of both PCA and SPCA‐based techniques are illustrated using the benchmark continuous stirred tank heater (CSTH) process. The results show that the PCs derived based on the proposed criterion has a better fault diagnosis ability.

中文翻译：

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用于稀疏主成分分析（SPCA）的非零载荷选择的新准则

稀疏主成分分析（SPCA）最近作为一种旨在通过抑制杂散值并克服传统主成分分析（PCA）的局限性而产生紧凑的主成分载荷的方法而出现。提出了一种基于SPCA的故障检测与诊断方法。在这种方法中，基于错误警报率（FAR）和故障检测率（FDR）来选择SPCA的非零负载（NNZL）数量。标准是具有较低的FAR和较高的FDR。这项新功能使SPCA更适合FDD，这可以通过将其性能与其他三种查找负载的方法进行比较来证明。使用基准连续搅拌釜加热器（CSTH）工艺说明了PCA和SPCA技术的整体FDD性能。