In this paper, we bdefly address the application of the standard principal component analysis （PCA） technique to fault detection and identification. Based on an analysis of the existing test statistic, we propose a ...In this paper, we bdefly address the application of the standard principal component analysis （PCA） technique to fault detection and identification. Based on an analysis of the existing test statistic, we propose a new test statistic, which is similar to the Hawkin＇s T2 H statistic but without the numerical drawback. In comparison with the SPE index, the threshold setting associated with the new statistic is computationally simpler. Our further study is dedicated to the analysis of fault sensitivity. We consider the off-set and scaling faults, and evaluate the test statistic by viewing its sensitivity to the faults. Our final study focuses on identifying off-set and scaling faults. To this end, two algorithms are proposed. This paper also includes some critical remarks on the application of the PCA technique to fault diagnosis.展开更多
心肌纤维化指细胞外基质纤维胶原蛋白的过度积累。纤维化是各种心肌病的关键特征,会影响心脏收缩和舒张功能。金属蛋白酶1组织抑制剂（tissue inhibitor of metalloproteinase-1,TIMP1）水平在心肌纤维化过程中始终增加,被认为是纤维化...心肌纤维化指细胞外基质纤维胶原蛋白的过度积累。纤维化是各种心肌病的关键特征,会影响心脏收缩和舒张功能。金属蛋白酶1组织抑制剂（tissue inhibitor of metalloproteinase-1,TIMP1）水平在心肌纤维化过程中始终增加,被认为是纤维化的标记物。展开更多
文摘In this paper, we bdefly address the application of the standard principal component analysis （PCA） technique to fault detection and identification. Based on an analysis of the existing test statistic, we propose a new test statistic, which is similar to the Hawkin＇s T2 H statistic but without the numerical drawback. In comparison with the SPE index, the threshold setting associated with the new statistic is computationally simpler. Our further study is dedicated to the analysis of fault sensitivity. We consider the off-set and scaling faults, and evaluate the test statistic by viewing its sensitivity to the faults. Our final study focuses on identifying off-set and scaling faults. To this end, two algorithms are proposed. This paper also includes some critical remarks on the application of the PCA technique to fault diagnosis.
