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SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis
SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis
Summary
SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis is a scholarly article[1].
Key Facts
SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis. Retrieved May 24, 2026, from https://4ort.xyz/entity/seformer-a-lightweight-cnn-transformer-based-on-separable-multiscale-depthwise-convolution-and-efficient-self-attention-
MLA“SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/seformer-a-lightweight-cnn-transformer-based-on-separable-multiscale-depthwise-convolution-and-efficient-self-attention-.
BibTeX@misc{4ortxyz_seformer-a-lightweight-cnn-transformer-based-on-separable-multiscale-depthwise-convolution-and-efficient-self-attention-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis}}, year = {2026}, url = {https://4ort.xyz/entity/seformer-a-lightweight-cnn-transformer-based-on-separable-multiscale-depthwise-convolution-and-efficient-self-attention-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis — https://4ort.xyz/entity/seformer-a-lightweight-cnn-transformer-based-on-separable-multiscale-depthwise-convolution-and-efficient-self-attention- (retrieved 2026-05-24)
