A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps
The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/11/1501 |
| _version_ | 1827639948496338944 |
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| author | Bo Zhang Zhenya Wang Ligang Yao Biaolin Luo |
| author_facet | Bo Zhang Zhenya Wang Ligang Yao Biaolin Luo |
| author_sort | Bo Zhang |
| collection | DOAJ |
| description | The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%. |
| first_indexed | 2024-03-09T16:51:12Z |
| format | Article |
| id | doaj.art-c5cd6bd3cd124b5b87459b19b3e57e34 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T16:51:12Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-c5cd6bd3cd124b5b87459b19b3e57e342023-11-24T14:40:56ZengMDPI AGEntropy1099-43002023-10-012511150110.3390/e25111501A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal PumpsBo Zhang0Zhenya Wang1Ligang Yao2Biaolin Luo3School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaSchool of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaSchool of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaThe real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.https://www.mdpi.com/1099-4300/25/11/1501self-priming centrifugal pumpfault diagnosisfluctuation dispersion entropymanifold mappingsupport vector machine |
| spellingShingle | Bo Zhang Zhenya Wang Ligang Yao Biaolin Luo A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps Entropy self-priming centrifugal pump fault diagnosis fluctuation dispersion entropy manifold mapping support vector machine |
| title | A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps |
| title_full | A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps |
| title_fullStr | A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps |
| title_full_unstemmed | A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps |
| title_short | A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps |
| title_sort | novel intelligent fault diagnosis method for self priming centrifugal pumps |
| topic | self-priming centrifugal pump fault diagnosis fluctuation dispersion entropy manifold mapping support vector machine |
| url | https://www.mdpi.com/1099-4300/25/11/1501 |
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