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| Support Vector Machine Classification Modeling for Acoustic Emission Detection of Valve Internal Leakage |
| WU Wen-kai,XU Ke-jun,YE Guo-yang |
| School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui 230009,China |
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Abstract Aiming at the problem of inaccurate regression modeling in the current research on acoustic emission detection of valve leakage, considering the practical application needs, a classification modeling study of valve liquid leakage is carried out. The mechanism and basic characteristics of valve leakage acoustic emission are analyzed, and a support vector machine(SVM) classification model for valve leakage acoustic emission signal feature quantity and leakage level is established. The valve leakage acoustic emission signal collection experiment is performed at the industrial production site, and the collected signals are pre-processed and feature extracted. The grid search method is used to find the optimal training parameters, and the optimal support vector machine classification model is established. The model prediction results show that the accuracy of valve leakage model prediction ang identification is more than 93%.
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Received: 31 December 2019
Published: 20 August 2021
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2021, Vol. 42 
