A Fault Diagnosis Method Based on Feature Pyramid CRNN Network
Aimed at the problems that the proportion and position of different fault characteristics of equipment components under variable working conditions and variable load in the signal are not fixed, and include the multi-scale complexity of the original vibration signal in a large number of different sc...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2022-02-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
| Subjects: | |
| Online Access: | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-2-182.shtml |
| _version_ | 1818918665044623360 |
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| author | LIU Xiuli, XU Xiaoli |
| author_facet | LIU Xiuli, XU Xiaoli |
| author_sort | LIU Xiuli, XU Xiaoli |
| collection | DOAJ |
| description | Aimed at the problems that the proportion and position of different fault characteristics of equipment components under variable working conditions and variable load in the signal are not fixed, and include the multi-scale complexity of the original vibration signal in a large number of different scenarios, a convolutional recurrent neural network (CRNN) rolling bearing fault diagnosis method based on feature pyramid network (FPN) was proposed. Using the convolution neural network (CNN) framework, the convolution layer of CNN and the long and short-term memory (LSTM) layer of recurrent neural network (RNN) were connected in parallel to form a new CRNN, so as to make full use of the learning ability of CNN to spatial domain information and RNN to time domain information. The weights were shared in each layer to reduce network parameters. A novel feature map was constructed using FPN, and one-dimensional signal and two-dimensional signal formed after stacking were input to extract the feature of the signal collected by the sensor, and realize fault diagnosis. The average diagnostic accuracy of this method is 99.20%, which is at least 3.62% higher than that of the basic neural network model, indicating that this method has a high diagnostic accuracy and a strong robustness. Using the bearing data set of Case Western Reserve University, it is proved that the method has a good universality. The t-SNE method was used to visually analyze the feature learning effect of the model. The results show that different fault category features have good clustering effect. |
| first_indexed | 2024-12-20T00:53:34Z |
| format | Article |
| id | doaj.art-85c83ea00b8242c5b414a0af680bf29a |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-12-20T00:53:34Z |
| publishDate | 2022-02-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-85c83ea00b8242c5b414a0af680bf29a2022-12-21T19:59:11ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672022-02-0156218219010.16183/j.cnki.jsjtu.2021.001A Fault Diagnosis Method Based on Feature Pyramid CRNN NetworkLIU Xiuli, XU Xiaoli0Key Laboratory of Modern Measurement and Control Technology of the Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, ChinaAimed at the problems that the proportion and position of different fault characteristics of equipment components under variable working conditions and variable load in the signal are not fixed, and include the multi-scale complexity of the original vibration signal in a large number of different scenarios, a convolutional recurrent neural network (CRNN) rolling bearing fault diagnosis method based on feature pyramid network (FPN) was proposed. Using the convolution neural network (CNN) framework, the convolution layer of CNN and the long and short-term memory (LSTM) layer of recurrent neural network (RNN) were connected in parallel to form a new CRNN, so as to make full use of the learning ability of CNN to spatial domain information and RNN to time domain information. The weights were shared in each layer to reduce network parameters. A novel feature map was constructed using FPN, and one-dimensional signal and two-dimensional signal formed after stacking were input to extract the feature of the signal collected by the sensor, and realize fault diagnosis. The average diagnostic accuracy of this method is 99.20%, which is at least 3.62% higher than that of the basic neural network model, indicating that this method has a high diagnostic accuracy and a strong robustness. Using the bearing data set of Case Western Reserve University, it is proved that the method has a good universality. The t-SNE method was used to visually analyze the feature learning effect of the model. The results show that different fault category features have good clustering effect.http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-2-182.shtmlconvolutional recurrent neural network (crnn)feature pyramidfault diagnosisfeature visualization |
| spellingShingle | LIU Xiuli, XU Xiaoli A Fault Diagnosis Method Based on Feature Pyramid CRNN Network Shanghai Jiaotong Daxue xuebao convolutional recurrent neural network (crnn) feature pyramid fault diagnosis feature visualization |
| title | A Fault Diagnosis Method Based on Feature Pyramid CRNN Network |
| title_full | A Fault Diagnosis Method Based on Feature Pyramid CRNN Network |
| title_fullStr | A Fault Diagnosis Method Based on Feature Pyramid CRNN Network |
| title_full_unstemmed | A Fault Diagnosis Method Based on Feature Pyramid CRNN Network |
| title_short | A Fault Diagnosis Method Based on Feature Pyramid CRNN Network |
| title_sort | fault diagnosis method based on feature pyramid crnn network |
| topic | convolutional recurrent neural network (crnn) feature pyramid fault diagnosis feature visualization |
| url | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-2-182.shtml |
| work_keys_str_mv | AT liuxiulixuxiaoli afaultdiagnosismethodbasedonfeaturepyramidcrnnnetwork AT liuxiulixuxiaoli faultdiagnosismethodbasedonfeaturepyramidcrnnnetwork |