A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing
Summary
A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing is a scholarly article[1].
Key Facts
A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-hybrid-1d-cnn-bi-lstm-based-model-with-spatial-dropout-for-multiple-fault-diagnosis-of-roller-bearing
MLA“A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-hybrid-1d-cnn-bi-lstm-based-model-with-spatial-dropout-for-multiple-fault-diagnosis-of-roller-bearing.
BibTeX@misc{4ortxyz_a-hybrid-1d-cnn-bi-lstm-based-model-with-spatial-dropout-for-multiple-fault-diagnosis-of-roller-bearing_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing}}, year = {2026}, url = {https://4ort.xyz/entity/a-hybrid-1d-cnn-bi-lstm-based-model-with-spatial-dropout-for-multiple-fault-diagnosis-of-roller-bearing}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing — https://4ort.xyz/entity/a-hybrid-1d-cnn-bi-lstm-based-model-with-spatial-dropout-for-multiple-fault-diagnosis-of-roller-bearing (retrieved 2026-05-24)
