A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines
The accurate prediction of incipient cavitation is of great significance for ensuring the stable operation of hydraulic turbines. Hydroacoustic signals contain essential information about the turbine’s operating state. Considering that traditional entropy methods are easily affected by environmental...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/3/538 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850203945140486144 |
|---|---|
| author | Mengge Lv Feng Li Yi Wang Tianzhen Wang Demba Diallo Xiaohang Wang |
| author_facet | Mengge Lv Feng Li Yi Wang Tianzhen Wang Demba Diallo Xiaohang Wang |
| author_sort | Mengge Lv |
| collection | DOAJ |
| description | The accurate prediction of incipient cavitation is of great significance for ensuring the stable operation of hydraulic turbines. Hydroacoustic signals contain essential information about the turbine’s operating state. Considering that traditional entropy methods are easily affected by environmental noise when the state pattern is chaotic, leading to the extracted cavitation features not being obvious, a Symbol Conditional Entropy (SCE) feature extraction method is proposed to classify the original variables according to different state patterns. The uncertainty is reduced, and the ability to extract fault information is improved, so more effective cavitation features can be extracted to describe the evolving trend of cavitation. The extracted cavitation features are used as indicators to predict incipient cavitation. In order to avoid missing critical information in the prediction process, an interval mean (IM) algorithm is proposed to determine the initial prediction point. The effectiveness of the proposed method is validated with hydroacoustic signals collected at the Harbin Institute of Large Electric Machinery. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of incipient cavitation prediction results decreased to 0.0018, 0.0015, and 1.59%, respectively. The RMSE, MAE, and MAPE of the proposed SCE decreased by 84.62%, 85.29%, and 87% compared with the Permutation Entropy (PE) method. The comparison results with different prediction algorithms show that the proposed SCE has excellent trend prediction performance and high precision. |
| format | Article |
| id | doaj-art-e735454ffc2a4b0286d98be4fb7f4089 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-e735454ffc2a4b0286d98be4fb7f40892025-08-20T02:11:24ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113353810.3390/jmse13030538A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic TurbinesMengge Lv0Feng Li1Yi Wang2Tianzhen Wang3Demba Diallo4Xiaohang Wang5Logistics Engineering College, Shanghai Maritime University, Pudong District, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Pudong District, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Pudong District, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Pudong District, Shanghai 201306, ChinaCentraleSupelec, CNRS, Group of Electrical Engineering Paris, Université Paris Saclay, 91192 Gif-sur-Yvette, FranceHarbin Electric Machinery Company Limited, Harbin 150040, ChinaThe accurate prediction of incipient cavitation is of great significance for ensuring the stable operation of hydraulic turbines. Hydroacoustic signals contain essential information about the turbine’s operating state. Considering that traditional entropy methods are easily affected by environmental noise when the state pattern is chaotic, leading to the extracted cavitation features not being obvious, a Symbol Conditional Entropy (SCE) feature extraction method is proposed to classify the original variables according to different state patterns. The uncertainty is reduced, and the ability to extract fault information is improved, so more effective cavitation features can be extracted to describe the evolving trend of cavitation. The extracted cavitation features are used as indicators to predict incipient cavitation. In order to avoid missing critical information in the prediction process, an interval mean (IM) algorithm is proposed to determine the initial prediction point. The effectiveness of the proposed method is validated with hydroacoustic signals collected at the Harbin Institute of Large Electric Machinery. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of incipient cavitation prediction results decreased to 0.0018, 0.0015, and 1.59%, respectively. The RMSE, MAE, and MAPE of the proposed SCE decreased by 84.62%, 85.29%, and 87% compared with the Permutation Entropy (PE) method. The comparison results with different prediction algorithms show that the proposed SCE has excellent trend prediction performance and high precision.https://www.mdpi.com/2077-1312/13/3/538hydroacoustic signalincipient cavitation predictionentropyinitial prediction pointhydraulic turbine |
| spellingShingle | Mengge Lv Feng Li Yi Wang Tianzhen Wang Demba Diallo Xiaohang Wang A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines Journal of Marine Science and Engineering hydroacoustic signal incipient cavitation prediction entropy initial prediction point hydraulic turbine |
| title | A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines |
| title_full | A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines |
| title_fullStr | A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines |
| title_full_unstemmed | A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines |
| title_short | A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines |
| title_sort | symbol conditional entropy based method for incipient cavitation prediction in hydraulic turbines |
| topic | hydroacoustic signal incipient cavitation prediction entropy initial prediction point hydraulic turbine |
| url | https://www.mdpi.com/2077-1312/13/3/538 |
| work_keys_str_mv | AT menggelv asymbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT fengli asymbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT yiwang asymbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT tianzhenwang asymbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT dembadiallo asymbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT xiaohangwang asymbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT menggelv symbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT fengli symbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT yiwang symbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT tianzhenwang symbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT dembadiallo symbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines AT xiaohangwang symbolconditionalentropybasedmethodforincipientcavitationpredictioninhydraulicturbines |