20.500.12556/RUL-146703 An improved MSCNN and GRU model for rolling bearing fault diagnosis Diagnosticiranje napak kotalnih ležajev na podlagi izboljšanih modelov MSCNN in NRU In this paper, a novel fault diagnosis method based on the fusion of squeeze and excitation-multiscale convolutional neural networks (SENet-MSCNN) and gate recurrent unit (GRU) is proposed to address the problem of low diagnosis rate caused by the fact that normal samples are much larger than fault samples in the vibration big data. The method takes the time-domain vibration signal as input and fuses the spatial features extracted by SENet-MSCNN. The temporal features extracted by GRU in order to bring them into the fully connected layer for identification so as to realize the intelligent diagnosis of rolling bearing adaptive feature extraction. Finally, the method is applied to the simulated signal and experimental data for testing and analysis. The results reveal that the model can reach 98.98 % and 76.44 % migration diagnostic accuracy in bearing and gearbox datasets. At the same time, it has strong noise immunity, adaptivity, and robustness, providing an effective way for intelligent diagnosis of rolling bearing vibration big data. SENet multiscale convolutional neural networks gate recurrent unit rolling bearings fault diagnosis SENet MSCNN GRU kotalni ležaji diagnosticiranje napak true false true Angleški jezik Slovenski jezik Neznano 2023-06-08 12:27:24 2023-06-08 12:27:30 2023-06-12 11:32:37 0000-00-00 00:00:00 2023 0 0 Str. 261-274 no. 5/6 Vol. 69 May/Jun. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.8 0039-2480 10.5545/sv-jme.2022.459 154984707 Wang...-_An_improved_MSCNN_and_GRU_model_for_rolling_bearing_fault_diagnosis.pdf Wang...-_An_improved_MSCNN_and_GRU_model_for_rolling_bearing_fault_diagnosis.pdf 1 2DE87413A4781AFA512743A14E6E32C0 784f07b08c6c8412f5679226c9a029a059b5aa4f2cb42df117298c49821c0310 070ff4da-05e7-11ee-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=169965 https://www.sv-jme.eu/article/based-on-the-improved-mscnn-and-gru-model-for-rolling-bearing-fault-diagnosis/ 0 b5de5e24-0903-11ee-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=170044 Fakulteta za strojništvo Strojniški vestnik 0 0 0