Sensor fault detection and isolation techniques based on PCA

Due to its effectiveness and simplicity, principal components analysis (PCA) has been considered as a basic technique of multivariate statistical process control (MSPC) and it has been applied with a great success in the FDI domain. A set of independent latent variables named principal components (PCs) are created by a linear combination of the original system variables. In the field of sensor fault detection and diagnosis based on PCA, several types of monitoring statistics are well established for enhancing fault detection, where the filtered SPE and SWE statistics are used in this work. After detection, process malfunctions are identified by applying an adequate fault isolation technique. However, various approaches of fault isolation have been suggested in the research literature. The aim of this work is to provide a succinct study exhibiting the ability of three fault isolation methods such as backward elimination sensor identification, contributions charts and fault reconstruction approach to give the correct isolation results via a simulation example.