Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings
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 f...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/5067651 |
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| _version_ | 1849467882609573888 |
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| author | David Verstraete Andrés Ferrada Enrique López Droguett Viviana Meruane Mohammad Modarres |
| author_facet | David Verstraete Andrés Ferrada Enrique López Droguett Viviana Meruane Mohammad Modarres |
| author_sort | David Verstraete |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6a1f5a2d1674f71abdce3f4c9af7413 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-e6a1f5a2d1674f71abdce3f4c9af74132025-08-20T03:26:00ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/50676515067651Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element BearingsDavid Verstraete0Andrés Ferrada1Enrique López Droguett2Viviana Meruane3Mohammad Modarres4Department of Mechanical Engineering, University of Maryland, College Park, MD, USAComputer Science Department, University of Chile, Santiago, ChileDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USAMechanical Engineering Department, University of Chile, Santiago, ChileDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USATraditional 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.http://dx.doi.org/10.1155/2017/5067651 |
| spellingShingle | David Verstraete Andrés Ferrada Enrique López Droguett Viviana Meruane Mohammad Modarres Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings Shock and Vibration |
| title | Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings |
| title_full | Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings |
| title_fullStr | Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings |
| title_full_unstemmed | Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings |
| title_short | Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings |
| title_sort | deep learning enabled fault diagnosis using time frequency image analysis of rolling element bearings |
| url | http://dx.doi.org/10.1155/2017/5067651 |
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