Sensor fault detection and isolation by robust principal component analysis

Sensors are essential components of modern control systems. Any faults in sensors will affect the overall performance of a system because their effects can easily propagate to manipulative variables through feedback control loops and also disturb other process variables. The task for sensor validation is to detect and isolate faulty sensors and estimate fault magnitudes afterwards to provide fault-free values. Model-based methods constitute an important approach to sensor fault detection and isolation (FDI). A model-based approach consists in generating residuals as the difference between the measurements and the estimates provided by the relationships existing between the various variables of the process. The analysis of these residuals may lead to detect and isolate the faulty sensors. Almost all conventional model-based methods presume the knowledge of an accurate model of the system, e.g. transfer function or system matrices in the state space representation. Principal component Analysis (PCA) is a data-driven method which is particularly well adapted to reveal linear relationships among the plant variables without formulating them explicitly and has also been employed for system identification. PCA has some other nice features. It can handle high dimensional and correlated process variables, provides a natural solution to the errors-in-variables problem and includes disturbance decoupling (Li & Qin, 2001). Moreover in the FDI field, Gertler & McAvoy (1997) have shown a close link between PCA and parity space method. Principal component analysis (PCA) has then been applied successfully in the monitoring of complex systems (Chiang & Colegrove, 2007; Harkat et al., 2006; Kano & Nakagawa, 2008). 17