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• W2977053225 abstract "Rub-impact is a kind of serious malfunction, which often occurs in rotating machinery. The non-stationary rub-impact signals are always submerged in the background and noise signals, which makes it difficult to accurately diagnose the rubbing based on the hand-designed features extracted by the traditional methods. This paper presents a 1-D convolutional neural network (CNN) based approach to automatically learn useful features for rub-impact fault diagnosis from the raw vibration signals of a rotor system. The proposed model is trained on a dataset of vibration signals obtained from an industrial hydro turbine rotor. The results show that timely and accurate rub-impact fault detection can be achieved by a simple 1-D CNN configuration." @default.
• W2977053225 created "2019-10-03" @default.
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• W2977053225 date "2020-08-01" @default.
• W2977053225 modified "2023-10-15" @default.
• W2977053225 title "Rub-Impact Fault Diagnosis of Rotating Machinery Based on 1-D Convolutional Neural Networks" @default.
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• W2977053225 doi "https://doi.org/10.1109/jsen.2019.2944157" @default.
• W2977053225 hasPublicationYear "2020" @default.
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