Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. However, this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed.
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