Abstract
The article is devoted to the problem of diagnosing the refueling machine designed for moving nuclear fuel to a nuclear power plant. We describe an approach to detecting anomalies in diagnostic signals that combines the capabilities of two methods - spectral analysis and the principal component method. The proposed approach takes into account multiperiodic of analyzed signals, modifying signal characteristics in the functioning of refueling machine in different standard modes and fluctuation of characteristics. It is shown that the reduction of the dimension and classification of the initial diagnostic information can be achieved by clustering the spectral characteristics in the space of the principal components. The advantages of the proposed approach are the small amount of processed data and the simplicity of computational procedures.
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