【摘 要】
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Principal Component Analysis (PCA) has been found wide applications. However, when standard PCA is applied to slow or parameter-varying process, it will lead to many false alarms. In this paper, to de
【机 构】
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School of Physics & Electronic, Central South University, Changsha, 410083
论文部分内容阅读
Principal Component Analysis (PCA) has been found wide applications. However, when standard PCA is applied to slow or parameter-varying process, it will lead to many false alarms. In this paper, to deal with the problem, we propose one recursive PCA algorithm, which efficiently updates the covariance matrix and will decrease the computation cost. The algorithm is based on First-Order Perturbation (FOP) theory, which is a rank-one update of the eigenvalues and its corresponding eigenvectors of a observation covariance matrix. We also propose two new statistics, one of which is similar to the Hawkin’s statistic but without the numerical drawback motivated by an analysis of the existing test statistic. In comparison with the SPE index, the threshold of the new statistic is computationally simpler. The effectiveness of the proposed RPCA algorithm and tow new statistics has been evaluated with an application of monitoring a continuous stirred tank heater simulation system.
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