1D CNN-Based Transfer Learning Model for Bearing Fault Diagnosis Under Variable Working Conditions

Md Junayed Hasan; Muhammad Sohaib; Jong-Myon Kim

doi:10.1007/978-3-030-03302-6_2

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1D CNN-Based Transfer Learning Model for Bearing Fault Diagnosis Under Variable Working Conditions

Md Junayed Hasan Muhammad Sohaib Jong-Myon Kim

Year: 2018 Advances in intelligent systems and computing Pages: 13-23 Publisher: Springer Nature

DOI: 10.1007/978-3-030-03302-6_2

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Keywords:

Computer science Fault (geology) Convolutional neural network Transfer of learning Bearing (navigation) Artificial intelligence Frequency domain Pattern recognition (psychology) Feature extraction Field (mathematics) Domain (mathematical analysis) Time domain Vibration SIGNAL (programming language) Machine learning Computer vision Mathematics 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

Gear and Bearing Dynamics Analysis

Physical Sciences → Engineering → Mechanical Engineering

1D CNN-Based Transfer Learning Model for Bearing Fault Diagnosis Under Variable Working Conditions

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