Work

SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

Year: 2024

Type: article

Abstract: Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence a... | Find, read and cite all the research you need on Tech Science Press

Source: Computers, materials & continua/Computers, materials & continua (Print)

DOI: 10.32604/cmc.2024.058785

Authors: Hongxing Wang, Hua Zhu, Huafeng Li, Xilai Ju

Institutions: Nanyang Technological University, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Award: G7417003958