Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification
As one of the most vital parts of rotating equipment, it is an essential work to diagnose rolling bearing failure. The traditional signal processing-based rolling bearing fault diagnosis algorithms rely on artificial feature extraction and expert knowledge. The working condition of rolling bearings...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/1971945 |
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| author | Mingxing Jia Yuemei Xu Maoyi Hong Xiyu Hu |
| author_facet | Mingxing Jia Yuemei Xu Maoyi Hong Xiyu Hu |
| author_sort | Mingxing Jia |
| collection | DOAJ |
| description | As one of the most vital parts of rotating equipment, it is an essential work to diagnose rolling bearing failure. The traditional signal processing-based rolling bearing fault diagnosis algorithms rely on artificial feature extraction and expert knowledge. The working condition of rolling bearings is complex and changeable, so the traditional algorithm is slightly lacking adaptability. The damage degree also plays a crucial role in fault monitoring. Different damage degrees may take different remedial measures, but traditional fault-diagnosis algorithms roughly divide the damage degree into several categories, which do not correspond to the continuous value of the damage degree. To solve the abovementioned two problems, this paper proposes a fault-diagnosis algorithm based on “end-to-end” one-dimensional convolutional neural network. The one-dimensional convolution kernel and the pooling layer are directly applied to the original time domain signal. Feature extraction and classifier are merged together, and the extracted features are used to judge the damage degree at the same time. Then, the generalization ability of the model is studied under a variety of conditions. Experiments show that the algorithm can achieve more than 99% accuracy and can accurately give the damage degree of the bearing. It has good performance under different speeds, different types of motors, and different sampling frequencies, and so it has good generalization ability. |
| format | Article |
| id | doaj-art-2bd62d872d9e420da19cbb67e1c6e7c5 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-2bd62d872d9e420da19cbb67e1c6e7c52025-02-03T00:59:39ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/19719451971945Multitask Convolutional Neural Network for Rolling Element Bearing Fault IdentificationMingxing Jia0Yuemei Xu1Maoyi Hong2Xiyu Hu3College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, ChinaShenyang Special Equipment Inspection and Research Institute, Shenyang 110819, Liaoning, ChinaAs one of the most vital parts of rotating equipment, it is an essential work to diagnose rolling bearing failure. The traditional signal processing-based rolling bearing fault diagnosis algorithms rely on artificial feature extraction and expert knowledge. The working condition of rolling bearings is complex and changeable, so the traditional algorithm is slightly lacking adaptability. The damage degree also plays a crucial role in fault monitoring. Different damage degrees may take different remedial measures, but traditional fault-diagnosis algorithms roughly divide the damage degree into several categories, which do not correspond to the continuous value of the damage degree. To solve the abovementioned two problems, this paper proposes a fault-diagnosis algorithm based on “end-to-end” one-dimensional convolutional neural network. The one-dimensional convolution kernel and the pooling layer are directly applied to the original time domain signal. Feature extraction and classifier are merged together, and the extracted features are used to judge the damage degree at the same time. Then, the generalization ability of the model is studied under a variety of conditions. Experiments show that the algorithm can achieve more than 99% accuracy and can accurately give the damage degree of the bearing. It has good performance under different speeds, different types of motors, and different sampling frequencies, and so it has good generalization ability.http://dx.doi.org/10.1155/2020/1971945 |
| spellingShingle | Mingxing Jia Yuemei Xu Maoyi Hong Xiyu Hu Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification Shock and Vibration |
| title | Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification |
| title_full | Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification |
| title_fullStr | Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification |
| title_full_unstemmed | Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification |
| title_short | Multitask Convolutional Neural Network for Rolling Element Bearing Fault Identification |
| title_sort | multitask convolutional neural network for rolling element bearing fault identification |
| url | http://dx.doi.org/10.1155/2020/1971945 |
| work_keys_str_mv | AT mingxingjia multitaskconvolutionalneuralnetworkforrollingelementbearingfaultidentification AT yuemeixu multitaskconvolutionalneuralnetworkforrollingelementbearingfaultidentification AT maoyihong multitaskconvolutionalneuralnetworkforrollingelementbearingfaultidentification AT xiyuhu multitaskconvolutionalneuralnetworkforrollingelementbearingfaultidentification |