CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis
Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction meth...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/1999-4893/18/3/148 |
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| author | Ruixue Wang Ning Zhao |
| author_facet | Ruixue Wang Ning Zhao |
| author_sort | Ruixue Wang |
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| description | Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. The improved Hippopotamus Optimization algorithm initializes the hippopotamus population with Tent chaotic mapping, designs an adaptive weight factor, and incorporates adaptive variation perturbation. Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. The prediction effects of the seven models, IHO-SVM. HO-SVM, PSO-SVM, GWO-SVM, WOA-SVM, SSA-SVM, and SVM were compared and analyzed with actual data. As a result, the comparison indicated that IHO-SVM has desirable robustness and generalization, which successfully improves the classification efficiency and the recognition rate in fault diagnosis. |
| format | Article |
| id | doaj-art-a5d6ccfd91c14fa0b8e4a441a33263d6 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-a5d6ccfd91c14fa0b8e4a441a33263d62025-08-20T03:40:43ZengMDPI AGAlgorithms1999-48932025-03-0118314810.3390/a18030148CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak DiagnosisRuixue Wang0Ning Zhao1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaDue to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. The improved Hippopotamus Optimization algorithm initializes the hippopotamus population with Tent chaotic mapping, designs an adaptive weight factor, and incorporates adaptive variation perturbation. Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. The prediction effects of the seven models, IHO-SVM. HO-SVM, PSO-SVM, GWO-SVM, WOA-SVM, SSA-SVM, and SVM were compared and analyzed with actual data. As a result, the comparison indicated that IHO-SVM has desirable robustness and generalization, which successfully improves the classification efficiency and the recognition rate in fault diagnosis.https://www.mdpi.com/1999-4893/18/3/148Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)improved hippopotamus optimization (IHO)SVMvalve leakage |
| spellingShingle | Ruixue Wang Ning Zhao CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis Algorithms Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) improved hippopotamus optimization (IHO) SVM valve leakage |
| title | CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis |
| title_full | CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis |
| title_fullStr | CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis |
| title_full_unstemmed | CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis |
| title_short | CEEMDAN-IHO-SVM: A Machine Learning Research Model for Valve Leak Diagnosis |
| title_sort | ceemdan iho svm a machine learning research model for valve leak diagnosis |
| topic | Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) improved hippopotamus optimization (IHO) SVM valve leakage |
| url | https://www.mdpi.com/1999-4893/18/3/148 |
| work_keys_str_mv | AT ruixuewang ceemdanihosvmamachinelearningresearchmodelforvalveleakdiagnosis AT ningzhao ceemdanihosvmamachinelearningresearchmodelforvalveleakdiagnosis |