Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network
This paper addresses the challenges of low accuracy and long transfer learning time in small-sample bearing fault diagnosis, which are often caused by limited samples, high noise levels, and poor feature extraction. We propose a method that combines an improved capsule network with a Siamese neural...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/92 |
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| author | Jarula Yasenjiang Yang Xiao Chao He Luhui Lv Wenhao Wang |
| author_facet | Jarula Yasenjiang Yang Xiao Chao He Luhui Lv Wenhao Wang |
| author_sort | Jarula Yasenjiang |
| collection | DOAJ |
| description | This paper addresses the challenges of low accuracy and long transfer learning time in small-sample bearing fault diagnosis, which are often caused by limited samples, high noise levels, and poor feature extraction. We propose a method that combines an improved capsule network with a Siamese neural network. Multi-view data partitioning is used to enrich data diversity, and Markov transformation converts one-dimensional vibration signals into two-dimensional images, enhancing the visualization of signal features. The dynamic routing mechanism of the capsule network effectively captures and integrates key fault features, improving the model’s feature representation and robustness. The Siamese network shares weights to optimize feature matching, while SKNet dynamically adjusts feature fusion to enhance generalization performance. By integrating the Siamese neural network with SKNet, we improve transfer efficiency, reduce the number of parameters, and lighten the model to reduce complexity and shorten transfer time. Experimental results demonstrate that this method can accurately identify faults under conditions of limited samples and high noise, thereby improving diagnostic accuracy and reducing transfer time. |
| format | Article |
| id | doaj-art-9f5ab99e37f74be8ac5a8e9879ddf464 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9f5ab99e37f74be8ac5a8e9879ddf4642025-01-10T13:20:51ZengMDPI AGSensors1424-82202024-12-012519210.3390/s25010092Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural NetworkJarula Yasenjiang0Yang Xiao1Chao He2Luhui Lv3Wenhao Wang4College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, ChinaThis paper addresses the challenges of low accuracy and long transfer learning time in small-sample bearing fault diagnosis, which are often caused by limited samples, high noise levels, and poor feature extraction. We propose a method that combines an improved capsule network with a Siamese neural network. Multi-view data partitioning is used to enrich data diversity, and Markov transformation converts one-dimensional vibration signals into two-dimensional images, enhancing the visualization of signal features. The dynamic routing mechanism of the capsule network effectively captures and integrates key fault features, improving the model’s feature representation and robustness. The Siamese network shares weights to optimize feature matching, while SKNet dynamically adjusts feature fusion to enhance generalization performance. By integrating the Siamese neural network with SKNet, we improve transfer efficiency, reduce the number of parameters, and lighten the model to reduce complexity and shorten transfer time. Experimental results demonstrate that this method can accurately identify faults under conditions of limited samples and high noise, thereby improving diagnostic accuracy and reducing transfer time.https://www.mdpi.com/1424-8220/25/1/92small samplecapsule networkSiamese neural networkSKNet |
| spellingShingle | Jarula Yasenjiang Yang Xiao Chao He Luhui Lv Wenhao Wang Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network Sensors small sample capsule network Siamese neural network SKNet |
| title | Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network |
| title_full | Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network |
| title_fullStr | Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network |
| title_full_unstemmed | Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network |
| title_short | Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network |
| title_sort | fault diagnosis of bearings with small sample size using improved capsule network and siamese neural network |
| topic | small sample capsule network Siamese neural network SKNet |
| url | https://www.mdpi.com/1424-8220/25/1/92 |
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