The
article describes an approach to building a self-learning diagnostic algorithm.
The self-learning algorithm creates models of the object under consideration.
The models are formed periodically through a certain time period. The model
includes a set of functions that can describe whole object, or a part of the
object, or a specified functionality of the object. Thus, information about
fault location can be obtained. During operation of the object the algorithm
collects data received from sensors. Then the algorithm creates samples related
to steady state operation. Clustering of those samples is used for the
functions definition. Values of the functions in the centers of clusters are
stored in the computer’s memory. To illustrate the considered approach, its
application to the diagnosis of turbomachines is described.
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