Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning
Large-scale mechanical equipment monitoring involves various kinds and quantities of information, and the present research on multisensor information fusion may face problems of information conflicts and modeling complexity. This paper proposes an analysis method combining correlation analysis and d...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8898944 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832553301864873984 |
|---|---|
| author | Tangbo Bai Jianwei Yang Lixiang Duan Yanxue Wang |
| author_facet | Tangbo Bai Jianwei Yang Lixiang Duan Yanxue Wang |
| author_sort | Tangbo Bai |
| collection | DOAJ |
| description | Large-scale mechanical equipment monitoring involves various kinds and quantities of information, and the present research on multisensor information fusion may face problems of information conflicts and modeling complexity. This paper proposes an analysis method combining correlation analysis and deep learning. According to the characteristics of monitoring data, three types of correlation coefficients between sensors in different states are obtained, and a new composite correlation analytical matrix is established to fuse the multisource heterogeneous data. The matrix represents fault feature information of different equipment states and helps further image generation. Meanwhile, a convolutional neural network-based deep learning method is developed to process the matrix and to discover the relationship between results and equipment states for fault diagnosis. To verify the method of this paper, experimental and field case studies are performed. The results show that it can accurately identify fault states and has higher diagnostic efficiency and accuracy than traditional methods. |
| format | Article |
| id | doaj-art-f0c2d700e5ec4483936d59dfe81b67b3 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-f0c2d700e5ec4483936d59dfe81b67b32025-02-03T05:54:25ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88989448898944Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep LearningTangbo Bai0Jianwei Yang1Lixiang Duan2Yanxue Wang3School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaCollege of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaLarge-scale mechanical equipment monitoring involves various kinds and quantities of information, and the present research on multisensor information fusion may face problems of information conflicts and modeling complexity. This paper proposes an analysis method combining correlation analysis and deep learning. According to the characteristics of monitoring data, three types of correlation coefficients between sensors in different states are obtained, and a new composite correlation analytical matrix is established to fuse the multisource heterogeneous data. The matrix represents fault feature information of different equipment states and helps further image generation. Meanwhile, a convolutional neural network-based deep learning method is developed to process the matrix and to discover the relationship between results and equipment states for fault diagnosis. To verify the method of this paper, experimental and field case studies are performed. The results show that it can accurately identify fault states and has higher diagnostic efficiency and accuracy than traditional methods.http://dx.doi.org/10.1155/2020/8898944 |
| spellingShingle | Tangbo Bai Jianwei Yang Lixiang Duan Yanxue Wang Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning Shock and Vibration |
| title | Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning |
| title_full | Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning |
| title_fullStr | Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning |
| title_full_unstemmed | Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning |
| title_short | Fault Diagnosis Method Research of Mechanical Equipment Based on Sensor Correlation Analysis and Deep Learning |
| title_sort | fault diagnosis method research of mechanical equipment based on sensor correlation analysis and deep learning |
| url | http://dx.doi.org/10.1155/2020/8898944 |
| work_keys_str_mv | AT tangbobai faultdiagnosismethodresearchofmechanicalequipmentbasedonsensorcorrelationanalysisanddeeplearning AT jianweiyang faultdiagnosismethodresearchofmechanicalequipmentbasedonsensorcorrelationanalysisanddeeplearning AT lixiangduan faultdiagnosismethodresearchofmechanicalequipmentbasedonsensorcorrelationanalysisanddeeplearning AT yanxuewang faultdiagnosismethodresearchofmechanicalequipmentbasedonsensorcorrelationanalysisanddeeplearning |