Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM
The fault diagnosis of piezoelectric sensor patches is very important for the stability of the vibration control system and fault-tolerant control technology. In order to improve the accuracy of fault self-diagnosis of piezoelectric sensor patches, singular value decomposition (SVD) and Hilbert marg...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/8239198 |
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| _version_ | 1832559022790672384 |
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| author | Tian-bing Ma Zhou Qing Du Fei Liu Jian |
| author_facet | Tian-bing Ma Zhou Qing Du Fei Liu Jian |
| author_sort | Tian-bing Ma |
| collection | DOAJ |
| description | The fault diagnosis of piezoelectric sensor patches is very important for the stability of the vibration control system and fault-tolerant control technology. In order to improve the accuracy of fault self-diagnosis of piezoelectric sensor patches, singular value decomposition (SVD) and Hilbert marginal spectrum method are proposed to extract multiple features of each IMF component and conduct feature fusion, and a support vector machine (SVM) based on particle swarm optimization (PSO) is designed for fault identification of different eigenvalues. In the experiment, the broken and degumming piezoelectric patches are simulated. Firstly, under the excitation of the square wave signal with no noise signal, when the SVD value and the maximum amplitude of Hilbert marginal spectrum are used as the fusion eigenvalue together, the diagnostic results show that the recognition accuracy can reach 95%, compared with the recognition accuracy of 70% and 80%, respectively, when the two are used as eigenvalues alone; the recognition result under fusion eigenvalue is obviously better than that of the latter. Secondly, in order to highlight the effectiveness of this method, the aforementioned experiment is conducted under the excitation of the square wave signal interfered by 0.5 dBW–1 dBW noise signal. The experimental results show that the fault recognition of the fused eigenvalue under different noise intensity signals is still superior to that of the single eigenvalue. |
| format | Article |
| id | doaj-art-adc9c18b4d6f4dba9cbbcbec50a50fea |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-adc9c18b4d6f4dba9cbbcbec50a50fea2025-02-03T01:31:06ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/82391988239198Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVMTian-bing Ma0Zhou Qing1Du Fei2Liu Jian3College of Mechanical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, ChinaCollege of Mechanical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, ChinaCollege of Mechanical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, ChinaCollege of Mechanical Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, ChinaThe fault diagnosis of piezoelectric sensor patches is very important for the stability of the vibration control system and fault-tolerant control technology. In order to improve the accuracy of fault self-diagnosis of piezoelectric sensor patches, singular value decomposition (SVD) and Hilbert marginal spectrum method are proposed to extract multiple features of each IMF component and conduct feature fusion, and a support vector machine (SVM) based on particle swarm optimization (PSO) is designed for fault identification of different eigenvalues. In the experiment, the broken and degumming piezoelectric patches are simulated. Firstly, under the excitation of the square wave signal with no noise signal, when the SVD value and the maximum amplitude of Hilbert marginal spectrum are used as the fusion eigenvalue together, the diagnostic results show that the recognition accuracy can reach 95%, compared with the recognition accuracy of 70% and 80%, respectively, when the two are used as eigenvalues alone; the recognition result under fusion eigenvalue is obviously better than that of the latter. Secondly, in order to highlight the effectiveness of this method, the aforementioned experiment is conducted under the excitation of the square wave signal interfered by 0.5 dBW–1 dBW noise signal. The experimental results show that the fault recognition of the fused eigenvalue under different noise intensity signals is still superior to that of the single eigenvalue.http://dx.doi.org/10.1155/2019/8239198 |
| spellingShingle | Tian-bing Ma Zhou Qing Du Fei Liu Jian Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM Shock and Vibration |
| title | Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM |
| title_full | Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM |
| title_fullStr | Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM |
| title_full_unstemmed | Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM |
| title_short | Fault Diagnosis of Piezoelectric Sensor Patches for Vibration Control Based on Multifeature Fusion and Improved SVM |
| title_sort | fault diagnosis of piezoelectric sensor patches for vibration control based on multifeature fusion and improved svm |
| url | http://dx.doi.org/10.1155/2019/8239198 |
| work_keys_str_mv | AT tianbingma faultdiagnosisofpiezoelectricsensorpatchesforvibrationcontrolbasedonmultifeaturefusionandimprovedsvm AT zhouqing faultdiagnosisofpiezoelectricsensorpatchesforvibrationcontrolbasedonmultifeaturefusionandimprovedsvm AT dufei faultdiagnosisofpiezoelectricsensorpatchesforvibrationcontrolbasedonmultifeaturefusionandimprovedsvm AT liujian faultdiagnosisofpiezoelectricsensorpatchesforvibrationcontrolbasedonmultifeaturefusionandimprovedsvm |