Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF
Cavitation detection is particularly essential for operating efficiency and stability of pumps. In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neu...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/8768043 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552204569935872 |
|---|---|
| author | Liang Dong Kan Wu Jian-cheng Zhu Cui Dai Li-xin Zhang Jin-nan Guo |
| author_facet | Liang Dong Kan Wu Jian-cheng Zhu Cui Dai Li-xin Zhang Jin-nan Guo |
| author_sort | Liang Dong |
| collection | DOAJ |
| description | Cavitation detection is particularly essential for operating efficiency and stability of pumps. In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neural network is introduced to detect the cavitation status for centrifugal pumps. The cavitation performance and interior flow-borne noise are measured under three different flow conditions. Then, time-frequency domain analysis is performed on the interior flow-borne noise signal using WPD, and the energy coefficient of each node is calculated to determine the optimal decomposition frequency band. Six-feature parameters are extracted based on frequency-division statistics, including three time-domain features and three wavelet packet features. After that, the PCA is applied for dimensionality reduction. Finally, three cavitation statuses of noncavitation, inception cavitation, and serious cavitation are identified adopting RBF neural network. The results show that the comprehensive identification rate of the proposed method for three cavitation statuses reaches 98.2% with low identification error. The method based on interior flow-borne noise analysis can be well applied for on-line monitoring and diagnosis of pump industry. |
| format | Article |
| id | doaj-art-1313e97252be4a27bfae4dc38752d0c7 |
| 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-1313e97252be4a27bfae4dc38752d0c72025-02-03T05:59:15ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/87680438768043Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBFLiang Dong0Kan Wu1Jian-cheng Zhu2Cui Dai3Li-xin Zhang4Jin-nan Guo5Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaSchool of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, ChinaCavitation detection is particularly essential for operating efficiency and stability of pumps. In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neural network is introduced to detect the cavitation status for centrifugal pumps. The cavitation performance and interior flow-borne noise are measured under three different flow conditions. Then, time-frequency domain analysis is performed on the interior flow-borne noise signal using WPD, and the energy coefficient of each node is calculated to determine the optimal decomposition frequency band. Six-feature parameters are extracted based on frequency-division statistics, including three time-domain features and three wavelet packet features. After that, the PCA is applied for dimensionality reduction. Finally, three cavitation statuses of noncavitation, inception cavitation, and serious cavitation are identified adopting RBF neural network. The results show that the comprehensive identification rate of the proposed method for three cavitation statuses reaches 98.2% with low identification error. The method based on interior flow-borne noise analysis can be well applied for on-line monitoring and diagnosis of pump industry.http://dx.doi.org/10.1155/2019/8768043 |
| spellingShingle | Liang Dong Kan Wu Jian-cheng Zhu Cui Dai Li-xin Zhang Jin-nan Guo Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF Shock and Vibration |
| title | Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF |
| title_full | Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF |
| title_fullStr | Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF |
| title_full_unstemmed | Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF |
| title_short | Cavitation Detection in Centrifugal Pump Based on Interior Flow-Borne Noise Using WPD-PCA-RBF |
| title_sort | cavitation detection in centrifugal pump based on interior flow borne noise using wpd pca rbf |
| url | http://dx.doi.org/10.1155/2019/8768043 |
| work_keys_str_mv | AT liangdong cavitationdetectionincentrifugalpumpbasedoninteriorflowbornenoiseusingwpdpcarbf AT kanwu cavitationdetectionincentrifugalpumpbasedoninteriorflowbornenoiseusingwpdpcarbf AT jianchengzhu cavitationdetectionincentrifugalpumpbasedoninteriorflowbornenoiseusingwpdpcarbf AT cuidai cavitationdetectionincentrifugalpumpbasedoninteriorflowbornenoiseusingwpdpcarbf AT lixinzhang cavitationdetectionincentrifugalpumpbasedoninteriorflowbornenoiseusingwpdpcarbf AT jinnanguo cavitationdetectionincentrifugalpumpbasedoninteriorflowbornenoiseusingwpdpcarbf |