Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model
Because of its unique characteristics of small specific gravity, high strength, and corrosion resistance, the carbon fiber sucker rod has been widely used in petroleum production. However, there is still a lack of corresponding online testing methods to detect its integrity during the process of man...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5189 |
| _version_ | 1797443887687532544 |
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| author | Chenquan Hua Siwei Chen Guoyan Xu Yang Chen |
| author_facet | Chenquan Hua Siwei Chen Guoyan Xu Yang Chen |
| author_sort | Chenquan Hua |
| collection | DOAJ |
| description | Because of its unique characteristics of small specific gravity, high strength, and corrosion resistance, the carbon fiber sucker rod has been widely used in petroleum production. However, there is still a lack of corresponding online testing methods to detect its integrity during the process of manufacturing. Ultrasonic nondestructive testing has become one of the most accepted methods for inspection of homogeneous and fixed-thickness composites, or layered and fixed-interface-shape composites, but a carbon fiber sucker rod with multi-layered structures and irregular interlayer interfaces increases the difficulty of testing. In this paper, a novel defect detection method based on multi-sensor information fusion and a deep belief network (DBN) model was proposed to identify online its defects. A water-immersed ultrasonic array with 32 ultrasonic probes was designed to realize the online and full-coverage scanning of carbon fiber rods in radial and axial positions. Then, a multi-sensor information fusion method was proposed to integrate amplitudes and times-of-flight of the received ultrasonic pulse-echo signals with the spatial angle information of each probe into defect images with obvious defects including small cracks, transverse cracks, holes, and chapped cracks. Three geometric features and two texture features from the defect images characterizing the four types of defects were extracted. Finally, a DBN-based defect identification model was constructed and trained to identify the four types of defects of the carbon fiber rods. The testing results showed that the defect identification accuracy of the proposed method was 95.11%. |
| first_indexed | 2024-03-09T13:03:36Z |
| format | Article |
| id | doaj.art-3f7747c82c0146ef9b66bcb940160cfa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:03:36Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3f7747c82c0146ef9b66bcb940160cfa2023-11-30T21:51:02ZengMDPI AGSensors1424-82202022-07-012214518910.3390/s22145189Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN ModelChenquan Hua0Siwei Chen1Guoyan Xu2Yang Chen3College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, ChinaBecause of its unique characteristics of small specific gravity, high strength, and corrosion resistance, the carbon fiber sucker rod has been widely used in petroleum production. However, there is still a lack of corresponding online testing methods to detect its integrity during the process of manufacturing. Ultrasonic nondestructive testing has become one of the most accepted methods for inspection of homogeneous and fixed-thickness composites, or layered and fixed-interface-shape composites, but a carbon fiber sucker rod with multi-layered structures and irregular interlayer interfaces increases the difficulty of testing. In this paper, a novel defect detection method based on multi-sensor information fusion and a deep belief network (DBN) model was proposed to identify online its defects. A water-immersed ultrasonic array with 32 ultrasonic probes was designed to realize the online and full-coverage scanning of carbon fiber rods in radial and axial positions. Then, a multi-sensor information fusion method was proposed to integrate amplitudes and times-of-flight of the received ultrasonic pulse-echo signals with the spatial angle information of each probe into defect images with obvious defects including small cracks, transverse cracks, holes, and chapped cracks. Three geometric features and two texture features from the defect images characterizing the four types of defects were extracted. Finally, a DBN-based defect identification model was constructed and trained to identify the four types of defects of the carbon fiber rods. The testing results showed that the defect identification accuracy of the proposed method was 95.11%.https://www.mdpi.com/1424-8220/22/14/5189carbon fiber sucker rodmulti-sensor information fusiondeep belief networkimage identificationdefect identification |
| spellingShingle | Chenquan Hua Siwei Chen Guoyan Xu Yang Chen Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model Sensors carbon fiber sucker rod multi-sensor information fusion deep belief network image identification defect identification |
| title | Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model |
| title_full | Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model |
| title_fullStr | Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model |
| title_full_unstemmed | Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model |
| title_short | Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model |
| title_sort | defect detection method of carbon fiber sucker rod based on multi sensor information fusion and dbn model |
| topic | carbon fiber sucker rod multi-sensor information fusion deep belief network image identification defect identification |
| url | https://www.mdpi.com/1424-8220/22/14/5189 |
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