Bearing Defect Classification Algorithm Based on Autoencoder Neural Network
The postproduction defect classification and detection of bearings still relies on manual detection, which is time-consuming and tedious. To address this, we propose a bearing defect classification network based on an autoencoder to enhance the efficiency and accuracy of bearing defect detection. An...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/6680315 |
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| _version_ | 1832554075889074176 |
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| author | Manhuai Lu Yuanxiang Mou |
| author_facet | Manhuai Lu Yuanxiang Mou |
| author_sort | Manhuai Lu |
| collection | DOAJ |
| description | The postproduction defect classification and detection of bearings still relies on manual detection, which is time-consuming and tedious. To address this, we propose a bearing defect classification network based on an autoencoder to enhance the efficiency and accuracy of bearing defect detection. An improved autoencoder is used to reduce dimension feature extraction and reduce large-scale images to small-scale images through encoder dimensional reduction. Defect classification is completed by feeding the extracted features into a convolutional classification network. Comparative experiments show that the neural network can effectively complete feature selection and substantially improve classification accuracy while avoiding the laborious algorithm of the conventional method. |
| format | Article |
| id | doaj-art-3b3f7c4292644de783c7e9dac668f95c |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-3b3f7c4292644de783c7e9dac668f95c2025-02-03T05:52:25ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/66803156680315Bearing Defect Classification Algorithm Based on Autoencoder Neural NetworkManhuai Lu0Yuanxiang Mou1College of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 11545, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology, Chengdu 10614, ChinaThe postproduction defect classification and detection of bearings still relies on manual detection, which is time-consuming and tedious. To address this, we propose a bearing defect classification network based on an autoencoder to enhance the efficiency and accuracy of bearing defect detection. An improved autoencoder is used to reduce dimension feature extraction and reduce large-scale images to small-scale images through encoder dimensional reduction. Defect classification is completed by feeding the extracted features into a convolutional classification network. Comparative experiments show that the neural network can effectively complete feature selection and substantially improve classification accuracy while avoiding the laborious algorithm of the conventional method.http://dx.doi.org/10.1155/2020/6680315 |
| spellingShingle | Manhuai Lu Yuanxiang Mou Bearing Defect Classification Algorithm Based on Autoencoder Neural Network Advances in Civil Engineering |
| title | Bearing Defect Classification Algorithm Based on Autoencoder Neural Network |
| title_full | Bearing Defect Classification Algorithm Based on Autoencoder Neural Network |
| title_fullStr | Bearing Defect Classification Algorithm Based on Autoencoder Neural Network |
| title_full_unstemmed | Bearing Defect Classification Algorithm Based on Autoencoder Neural Network |
| title_short | Bearing Defect Classification Algorithm Based on Autoencoder Neural Network |
| title_sort | bearing defect classification algorithm based on autoencoder neural network |
| url | http://dx.doi.org/10.1155/2020/6680315 |
| work_keys_str_mv | AT manhuailu bearingdefectclassificationalgorithmbasedonautoencoderneuralnetwork AT yuanxiangmou bearingdefectclassificationalgorithmbasedonautoencoderneuralnetwork |