Fault Prediction of Centrifugal Pump Based on Improved KNN
To effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is p...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/7306131 |
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| _version_ | 1832561566594105344 |
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| author | YunFei Chen Jianping Yuan Yin Luo Wenqi Zhang |
| author_facet | YunFei Chen Jianping Yuan Yin Luo Wenqi Zhang |
| author_sort | YunFei Chen |
| collection | DOAJ |
| description | To effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is proposed. In this method, the Mahalanobis distance is used to replace the distance function in the conventional KNN algorithm. Grid search and cross-validation are used to determine the optimal K value of the prediction model. A centrifugal pump test rig was established to solve three common faults of centrifugal pumps: cavitation, impeller damage, and machine seal damage, and the method was verified. The results show that this method can effectively distinguish the specific fault types of centrifugal pumps based on vibration signals, and the fault prediction accuracy of the off-balance condition is up to 82%. This study provides a novel idea and method for centrifugal pump fault prediction and diagnosis and avoids the interaction between parameters when monitoring multiple parameters. |
| format | Article |
| id | doaj-art-60ede67fd96c4ba993d7408820fb6cc2 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-60ede67fd96c4ba993d7408820fb6cc22025-02-03T01:24:44ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/73061317306131Fault Prediction of Centrifugal Pump Based on Improved KNNYunFei Chen0Jianping Yuan1Yin Luo2Wenqi Zhang3Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212000, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212000, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212000, ChinaResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212000, ChinaTo effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is proposed. In this method, the Mahalanobis distance is used to replace the distance function in the conventional KNN algorithm. Grid search and cross-validation are used to determine the optimal K value of the prediction model. A centrifugal pump test rig was established to solve three common faults of centrifugal pumps: cavitation, impeller damage, and machine seal damage, and the method was verified. The results show that this method can effectively distinguish the specific fault types of centrifugal pumps based on vibration signals, and the fault prediction accuracy of the off-balance condition is up to 82%. This study provides a novel idea and method for centrifugal pump fault prediction and diagnosis and avoids the interaction between parameters when monitoring multiple parameters.http://dx.doi.org/10.1155/2021/7306131 |
| spellingShingle | YunFei Chen Jianping Yuan Yin Luo Wenqi Zhang Fault Prediction of Centrifugal Pump Based on Improved KNN Shock and Vibration |
| title | Fault Prediction of Centrifugal Pump Based on Improved KNN |
| title_full | Fault Prediction of Centrifugal Pump Based on Improved KNN |
| title_fullStr | Fault Prediction of Centrifugal Pump Based on Improved KNN |
| title_full_unstemmed | Fault Prediction of Centrifugal Pump Based on Improved KNN |
| title_short | Fault Prediction of Centrifugal Pump Based on Improved KNN |
| title_sort | fault prediction of centrifugal pump based on improved knn |
| url | http://dx.doi.org/10.1155/2021/7306131 |
| work_keys_str_mv | AT yunfeichen faultpredictionofcentrifugalpumpbasedonimprovedknn AT jianpingyuan faultpredictionofcentrifugalpumpbasedonimprovedknn AT yinluo faultpredictionofcentrifugalpumpbasedonimprovedknn AT wenqizhang faultpredictionofcentrifugalpumpbasedonimprovedknn |