Fault detection method based on principal component analysis and kernel density estimation and its application

Considering the characteristic of the imperial smelting furnace (ISF) data in abnormal distribution, the novel fault detection method based on the improved principal component analysis (PCA) is presented. Firstly, the data are preprocessed and the improved method for the abnormal value removal and the PCA model for the ISF are obtained. And then, the control limit of the PCA model is calculated by the method of multivariate kernel density estimation (KDE). The practical results show that compared with the features extracted by PCA, the proposed method helps to reduce the false alarm rate or missing alarm rate of the traditional PCA model, and increase the sensitivity of the monitoring process and improve the detection of the ISF.