Multi-sensor temporal-spatial graph network fusion empirical mode decomposition convolution for machine fault diagnosis

Kuangchi Sun; Aijun Yin

doi:10.1016/j.inffus.2024.102708

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Multi-sensor temporal-spatial graph network fusion empirical mode decomposition convolution for machine fault diagnosis

Kuangchi Sun Aijun Yin

Year: 2024 Journal: Information Fusion Vol: 114 Pages: 102708-102708 Publisher: Elsevier BV

DOI: 10.1016/j.inffus.2024.102708

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Keywords:

Computer science Hilbert–Huang transform Convolution (computer science) Fault (geology) Graph Mode (computer interface) Fusion Decomposition Data mining Artificial intelligence Algorithm Theoretical computer science Computer vision Artificial neural network Geology Seismology

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Topics

Machine Fault Diagnosis Techniques

Physical Sciences → Engineering → Control and Systems Engineering

Fault Detection and Control Systems

Physical Sciences → Engineering → Control and Systems Engineering

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Multi-sensor temporal-spatial graph network fusion empirical mode decomposition convolution for machine fault diagnosis

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