A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions
Intelligent fault diagnosis is a promising tool for processing mechanical big data. It can quickly and efficiently process the collected signals and provide accurate diagnosis results. However, rotating machinery often works under various speed conditions, which makes it difficult to extract fault f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/8/1603 |
| _version_ | 1818023521126711296 |
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| author | Huijie Ma Shunming Li Zenghui An |
| author_facet | Huijie Ma Shunming Li Zenghui An |
| author_sort | Huijie Ma |
| collection | DOAJ |
| description | Intelligent fault diagnosis is a promising tool for processing mechanical big data. It can quickly and efficiently process the collected signals and provide accurate diagnosis results. However, rotating machinery often works under various speed conditions, which makes it difficult to extract fault features. Inspired by speech recognition, the nuisance attribute projection method in speech recognition is introduced into fault diagnosis to solve the problem of feature extraction in variable speed signals. Based on the idea of unsupervised feature learning, the loss function of nuisance attribute projection is added to the loss function of convolutional neural network (CNN) to learn fault features from original data. Health status is classified according to the learned characteristics and projection matrix P. A special designed bearing dataset is employed to verify the effectiveness of the proposed method. The results show that the proposed method has a higher accuracy and a simpler framework, which is superior to the existing methods in bearing fault diagnosis. |
| first_indexed | 2024-12-10T03:45:38Z |
| format | Article |
| id | doaj.art-8bedec4e259d43a987ac5e7ff8be43e7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T03:45:38Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8bedec4e259d43a987ac5e7ff8be43e72022-12-22T02:03:26ZengMDPI AGApplied Sciences2076-34172019-04-0198160310.3390/app9081603app9081603A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed ConditionsHuijie Ma0Shunming Li1Zenghui An2College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaIntelligent fault diagnosis is a promising tool for processing mechanical big data. It can quickly and efficiently process the collected signals and provide accurate diagnosis results. However, rotating machinery often works under various speed conditions, which makes it difficult to extract fault features. Inspired by speech recognition, the nuisance attribute projection method in speech recognition is introduced into fault diagnosis to solve the problem of feature extraction in variable speed signals. Based on the idea of unsupervised feature learning, the loss function of nuisance attribute projection is added to the loss function of convolutional neural network (CNN) to learn fault features from original data. Health status is classified according to the learned characteristics and projection matrix P. A special designed bearing dataset is employed to verify the effectiveness of the proposed method. The results show that the proposed method has a higher accuracy and a simpler framework, which is superior to the existing methods in bearing fault diagnosis.https://www.mdpi.com/2076-3417/9/8/1603rolling bearingfault diagnosisvariable speed conditionconvolutional neural networknuisance attribute projection |
| spellingShingle | Huijie Ma Shunming Li Zenghui An A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions Applied Sciences rolling bearing fault diagnosis variable speed condition convolutional neural network nuisance attribute projection |
| title | A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions |
| title_full | A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions |
| title_fullStr | A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions |
| title_full_unstemmed | A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions |
| title_short | A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions |
| title_sort | fault diagnosis approach for rolling bearing based on convolutional neural network and nuisance attribute projection under various speed conditions |
| topic | rolling bearing fault diagnosis variable speed condition convolutional neural network nuisance attribute projection |
| url | https://www.mdpi.com/2076-3417/9/8/1603 |
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