Fault detection and isolation is crucial for the efficient operation and safety of any industrial process. There is a variety\nof methods from all areas of data analysis employed to solve this kind of task, such as Bayesian reasoning and Kalman\nfilter. In this paper, the authors use a discrete Field Kalman Filter (FKF) to detect and recognize faulty conditions in a\nsystem. The proposed approach, devised for stochastic linear systems, allows for analysis of faults that can be\nexpressed both as parameter and disturbance variations. This approach is formulated for the situations when the fault\ncatalog is known, resulting in the algorithm allowing estimation of probability values. Additionally, a variant of\nalgorithm with greater numerical robustness is presented, based on computation of logarithmic odds. Proposed\nalgorithm operation is illustrated with numerical examples, and both its merits and limitations are critically discussed\nand compared with traditional EKF.
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