A new online fault detection method based on PCA technique

In this paper, we suggest an extension of a previous study in Recursive Singular Spectrum Analysis (RSSA) (Hongli & Hui-Jun (2012) Fault detection for Markovian jump systems with sensor saturations and randomly varying non-linearities. IEEE Trans. Circuits Syst. I: Regul. Pap., 59, 2354-2362) to an online method for fault detection. This extended method is based on first-order perturbation (FOP) theory where the eigenvalues and eigenvectors of the foregoing covariance matrix are updated taking into account the effect of new acquired data which are considered as perturbation in the actual covariance matrix. This proposed diagnosis method is entitled 'recursive principal component analysis based on FOP' (RPCA-FOP) and is compared with other PCA techniques existing in literature such as the conventional PCA and the sliding window PCA where the average computation time, the missed detection rate and the false alarm rate are evaluated for each method.