A Multi-Information Fusion ViT Model and Its Application to the Fault Diagnosis of Bearing with Small Data Samples
نویسندگان
چکیده
To solve the fault diagnosis difficulty of bearings with small data samples, a novel multi-information fusion vision transformer (ViT) model based on time–frequency representation (TFR) maps is proposed in this paper. The original vibration signal decomposed into different scale sub-signals by discrete wavelet transforms (DWTs), and continuous (CWTs) are used to transform these maps, which concatenated input ViT diagnose bearing fault. Through multifaceted experiment analysis results demonstrate that can strong generalization robustness; its average accuracy achieved 99.85%, it was superior other methods, such as CNN, one-dimensional signal, TFR signal.
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ژورنال
عنوان ژورنال: Machines
سال: 2023
ISSN: ['2075-1702']
DOI: https://doi.org/10.3390/machines11020277