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.
Qinghua Zhang (2021). Sparse fault diagnosis (https://www.mathworks.com/matlabcentral/fileexchange/89847-sparse-fault-diagnosis), MATLAB Central File Exchange. Retrieved .
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