| Autor: |
LI Kai-xuan, ZHANG Yu-qi, FU Chun-jian, DUAN Yue-chen |
| Předmět: |
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| Zdroj: |
China Rural Water & Hydropower; 2024, Issue 2, p96-102, 7p |
| Abstrakt: |
Aiming at the problems of difficult and inefficient safety maintenance of hydraulic gates, this paper proposes an information fusion fault diagnosis method for hydraulic gates based on support vector machine (SVM) and improved D-S evidence theory. The method constructs feature subspaces by extracting information entropy features of wavelet packets of different sensor diagnostic signals, and then constructs diagnostic sub networks in each feature subspace. Finally, the input of each diagnostic sub-network is fused at the decision-making level by using improved evidence theory, so as to achieve the multi-information fusion diagnosis results of hydraulic gates. The experimental results of gate fault diagnosis show that the information fusion gate fault diagnosis method can effectively identify the types of radial gate faults, with a fault diagnosis accuracy of 98.33%, and high diagnostic reliability. The diagnostic uncertainty of various faults is less than 1%. The experimental results verify the feasibility of using intelligent fault diagnosis methods in the field of hydraulic gates, which is of great significance for improving the troubleshooting methods of hydraulic gates and promoting the development of intelligent water conservancy engineering. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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