The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

Shaojiang Dong; Kun He; Baoping Tang

doi:10.1007/s40430-020-02661-3

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The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

Shaojiang Dong Kun He Baoping Tang

Year: 2020 Journal: Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol: 42 (11) Publisher: Springer Science+Business Media

DOI: 10.1007/s40430-020-02661-3

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Keywords:

Bearing (navigation) Fault (geology) Feature extraction Vibration Autoencoder Artificial intelligence Pattern recognition (psychology) Frequency domain SIGNAL (programming language) Feature (linguistics) Computer science Engineering Control theory (sociology) Deep learning Computer vision Acoustics

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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

Engineering Diagnostics and Reliability

Physical Sciences → Engineering → Mechanics of Materials

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

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