Sensor fault detection and identification using Kernel PCA and its fast data reconstruction

In this paper, a novel sensor fault detection and identification technique based on kernel principal component analysis (KPCA) and its fast data reconstruction is presented. Although it has been proved that KPCA shows a better performance for sensor fault detection, the fault identification method has rarely been developed. Using the fast data reconstruction based on distance constraint, we employ the residuals of variables to identify the faulty sensor. Since the proposed method does not include iterative calculation, it has a lower calculation burden and is more suitable for online application. The simulation results show that the proposed method effectively identifies the source of typical sensor faults.