Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network
Intelligent operation and maintenance is an important part of Industry 4.0. In order to realize the intelligent of plant equipment, it will make full use of artificial intelligence methods to evaluate the operating status of the equipment. Fault diagnosis of industrial equipment represented by beari...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9345801/ |
| _version_ | 1818933774610595840 |
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| author | Jiaxing Wang Dazhi Wang Sihan Wang Wenhui Li Keling Song |
| author_facet | Jiaxing Wang Dazhi Wang Sihan Wang Wenhui Li Keling Song |
| author_sort | Jiaxing Wang |
| collection | DOAJ |
| description | Intelligent operation and maintenance is an important part of Industry 4.0. In order to realize the intelligent of plant equipment, it will make full use of artificial intelligence methods to evaluate the operating status of the equipment. Fault diagnosis of industrial equipment represented by bearings is critical in smart manufacturing. Early, online and accurate diagnostics can save the plant a lot of time and expense. With the development of sensor technology and deep learning technology, multi-sensor information fusion and convolutional neural network (CNN) provide a solution to the above problems. In this paper, based on the characteristics of mechanical vibration signal propagation in space, a new multi-sensor information fusion method is proposed to implement fault classification. This method constructs the time-domain vibration signals of multiple sensors from different position into a rectangular two-dimensional matrix, and then uses an improved 2D CNN to realize signal classification. The method is validated on the open dataset Case Western Reserve University, the University of Cincinnati IMS bearing database and the dataset form designed bearing fault test rig, has achieved prediction of 99.92%, 99.68%, and 99.25% respectively. Compared with the traditional 1D, 2D CNN and other fault classification methods, the model can utilize less data and computational complexity, achieve higher fault prediction accuracy. |
| first_indexed | 2024-12-20T04:53:44Z |
| format | Article |
| id | doaj.art-ee0632c2b1584057bc54eb986e3ee610 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:53:44Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ee0632c2b1584057bc54eb986e3ee6102022-12-21T19:52:48ZengIEEEIEEE Access2169-35362021-01-019237172372510.1109/ACCESS.2021.30567679345801Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural NetworkJiaxing Wang0Dazhi Wang1https://orcid.org/0000-0001-8067-9313Sihan Wang2Wenhui Li3https://orcid.org/0000-0002-0385-2132Keling Song4College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaChina North Vehicle Research Institute, Beijing, ChinaIntelligent operation and maintenance is an important part of Industry 4.0. In order to realize the intelligent of plant equipment, it will make full use of artificial intelligence methods to evaluate the operating status of the equipment. Fault diagnosis of industrial equipment represented by bearings is critical in smart manufacturing. Early, online and accurate diagnostics can save the plant a lot of time and expense. With the development of sensor technology and deep learning technology, multi-sensor information fusion and convolutional neural network (CNN) provide a solution to the above problems. In this paper, based on the characteristics of mechanical vibration signal propagation in space, a new multi-sensor information fusion method is proposed to implement fault classification. This method constructs the time-domain vibration signals of multiple sensors from different position into a rectangular two-dimensional matrix, and then uses an improved 2D CNN to realize signal classification. The method is validated on the open dataset Case Western Reserve University, the University of Cincinnati IMS bearing database and the dataset form designed bearing fault test rig, has achieved prediction of 99.92%, 99.68%, and 99.25% respectively. Compared with the traditional 1D, 2D CNN and other fault classification methods, the model can utilize less data and computational complexity, achieve higher fault prediction accuracy.https://ieeexplore.ieee.org/document/9345801/Convolutional neural network (CNN)fault diagnosismulti-sensor information fusion |
| spellingShingle | Jiaxing Wang Dazhi Wang Sihan Wang Wenhui Li Keling Song Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network IEEE Access Convolutional neural network (CNN) fault diagnosis multi-sensor information fusion |
| title | Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network |
| title_full | Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network |
| title_fullStr | Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network |
| title_full_unstemmed | Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network |
| title_short | Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network |
| title_sort | fault diagnosis of bearings based on multi sensor information fusion and 2d convolutional neural network |
| topic | Convolutional neural network (CNN) fault diagnosis multi-sensor information fusion |
| url | https://ieeexplore.ieee.org/document/9345801/ |
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