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- W4378808182 abstract "The article deals with the problem of classifying the states of functioning of a rotor system. In a continuous experiment, four different states of a sliding bearing unit were distinguished on the basis of thermogram images. ResNet convolutional neural network training was used for automated classification of thermogram images obtained with a thermal imager The data were partitioned into training and testing samples. Two types of partitioning were used: random one and sequential one. The data were partitioned in the ratio of 70% for training and 30% for testing. With sequential partitioning, the training sample was selected at the beginning of recording each individual experiment, and the test sample at the end. The best results were obtained when training the ResNet18 SNN. The test accuracy for the random method of data partitioning was 67.5%, and for the sequential method it was 54.6%." @default.
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- W4378808182 date "2023-01-01" @default.
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- W4378808182 title "Intelligent Diagnostic System for the Sliding Bearing Unit" @default.
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- W4378808182 doi "https://doi.org/10.1007/978-3-031-35510-3_54" @default.
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