Abstract
AbstractSparse 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.
Published Version
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