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• W4308191259 abstract "Abstract In view of the complexity of the engine mechanical structure and the diversity of faults, this paper presents a one-dimensional convolutional neural network (1DCNN)-vision transformer (ViT) ensemble model for identifying engine faults based on acoustic emission (AE) signals. The 1DCNN-ViT ensemble model combines 1DCNN and ViT. Firstly, AE signals of various faults are collected on the engine fault test rig. The dataset is constructed from its High-Mel Filterbank feature, which applies to AE signals. The proposed model has advantageous performance on this dataset. Secondly, the proposed model has a higher test accuracy than other new models. Finally, the fault data with different signal-to-noise ratios are input into the trained models, and the proposed model has better anti-noise performance. Overall, the proposed method can more accurately identify the AE signals of engine faults. It can be used as an effective method to diagnose engine faults." @default.
• W4308191259 created "2022-11-09" @default.
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• W4308191259 date "2022-11-17" @default.
• W4308191259 modified "2023-09-30" @default.
• W4308191259 title "Identification of engine faults based on acoustic emission signals using a 1DCNN-ViT ensemble model" @default.
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• W4308191259 doi "https://doi.org/10.1088/1361-6501/aca041" @default.
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