Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector
Acoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE ins...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/173470 |
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| _version_ | 1832563882855497728 |
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| author | Yibo Li Yuxiang Zhang Huiyu Zhu Rongxin Yan Yuanyuan Liu Liying Sun Zhoumo Zeng |
| author_facet | Yibo Li Yuxiang Zhang Huiyu Zhu Rongxin Yan Yuanyuan Liu Liying Sun Zhoumo Zeng |
| author_sort | Yibo Li |
| collection | DOAJ |
| description | Acoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE inspection. The drop-back signals are very difficult to filter out using conventional methods. To solve this problem, a novel AE inner detector, which works inside the storage tank, is adopted and a pattern recognition algorithm based on CRF (Conditional Random Field) model is presented. The algorithm is applied to differentiate the corrosion signals from interference signals, especially drop-back signals caused by condensation. Q235 steel corrosion signals and drop-signals were collected both in laboratory and in field site, and seven typical AE features based on hits and frequency are extracted and selected by mRMR (Minimum Redundancy Maximum Relevance) for pattern recognition. To validate the effectiveness of the proposed algorithm, the recognition result of CRF model was compared with BP (Back Propagation), SVM (Support Vector Machine), and HMM (Hidden Markov Model). The results show that training speed, accuracy, and ROC (Receiver Operating Characteristic) results of the CRF model outperform other methods. |
| format | Article |
| id | doaj-art-f216a3dbbd1b47bab9b242186e43e3af |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-f216a3dbbd1b47bab9b242186e43e3af2025-02-03T01:12:23ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/173470173470Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner DetectorYibo Li0Yuxiang Zhang1Huiyu Zhu2Rongxin Yan3Yuanyuan Liu4Liying Sun5Zhoumo Zeng6State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaBeijing Institute of Spacecraft Environment Engineering, Beijing 100094, ChinaState Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaSchool of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaState Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, ChinaAcoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE inspection. The drop-back signals are very difficult to filter out using conventional methods. To solve this problem, a novel AE inner detector, which works inside the storage tank, is adopted and a pattern recognition algorithm based on CRF (Conditional Random Field) model is presented. The algorithm is applied to differentiate the corrosion signals from interference signals, especially drop-back signals caused by condensation. Q235 steel corrosion signals and drop-signals were collected both in laboratory and in field site, and seven typical AE features based on hits and frequency are extracted and selected by mRMR (Minimum Redundancy Maximum Relevance) for pattern recognition. To validate the effectiveness of the proposed algorithm, the recognition result of CRF model was compared with BP (Back Propagation), SVM (Support Vector Machine), and HMM (Hidden Markov Model). The results show that training speed, accuracy, and ROC (Receiver Operating Characteristic) results of the CRF model outperform other methods.http://dx.doi.org/10.1155/2015/173470 |
| spellingShingle | Yibo Li Yuxiang Zhang Huiyu Zhu Rongxin Yan Yuanyuan Liu Liying Sun Zhoumo Zeng Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector Shock and Vibration |
| title | Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector |
| title_full | Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector |
| title_fullStr | Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector |
| title_full_unstemmed | Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector |
| title_short | Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector |
| title_sort | recognition algorithm of acoustic emission signals based on conditional random field model in storage tank floor inspection using inner detector |
| url | http://dx.doi.org/10.1155/2015/173470 |
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