Sparse fault diagnosis

Dynamic System Sparse Fault Detection and Isolation

Dynamic system fault diagnosis is often faced with a large number of possible faults. To avoid intractable combinatorial problems, sparse estimation techniques appear to be a powerful tool for isolating faults, under the assumption that only a small number of possible faults can be simultaneously active. However, sparse estimation is often studied in the framework of linear algebraic equations, whereas model-based fault diagnosis is usually investigated for dynamic systems modeled with state equations involving internal states. These Matlab files illustrate how to establish a link between the above two formalisms through efficient and reliable algorithms, mainly based on advanced analyses of residuals generated with the Kalman and Kitanidis filters. One of the m-files relies on the function lasso.m of the Matlab Statistics Toolbox.