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Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings
Joint Authors
Meruane, V.
Verstraete, David
Ferrada, Andrés
Droguett, Enrique López
Modarres, Mohammad
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-09
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system.
This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results.
To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data.
Time-frequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis.
This methodology was applied to two public data sets of rolling element bearing vibration signals.
Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness.
The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.
American Psychological Association (APA)
Verstraete, David& Ferrada, Andrés& Droguett, Enrique López& Meruane, V.& Modarres, Mohammad. 2017. Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings. Shock and Vibration،Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1204589
Modern Language Association (MLA)
Verstraete, David…[et al.]. Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings. Shock and Vibration No. 2017 (2017), pp.1-17.
https://search.emarefa.net/detail/BIM-1204589
American Medical Association (AMA)
Verstraete, David& Ferrada, Andrés& Droguett, Enrique López& Meruane, V.& Modarres, Mohammad. Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings. Shock and Vibration. 2017. Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1204589
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204589