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| Monitoring of primary cavitation of centrifugal pump based on support vector machine |
| YE Tao1, SI Qiaorui1, SHEN Chunhao1, YANG Song2, YUAN Shouqi1* |
| 1. National Research Center of Pumps, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. Key Laboratory of Nuclear Reactor System Design Technology, Nuclear Power Institute of China, Chengdu, Sichuan 610213, China |
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Abstract The purpose of this paper is to use machine learning methods to monitor the running status of centrifugal pump, and to make judgments on the initial cavitation status before cavitation failures occur in the centrifugal pumps, so as to provide a research foundation for online monitoring technology of centrifugal pumps. The primary cavitation of centrifugal pump was studied based on support vector machine monitoring. The vibration signals of centrifugal pump were collected and the features selection of the mean, standard deviation, skewness, kurtosis were selected as the eigenvector training model. At the same time, the grid search was used to participate in K-CV cross-validation way to find the optimal combination of parameters. The results show that the grid optimization combined with cross validation method can find the optimal parameters. In the case of single feature training model, the average recognition rate of the standard deviation is the highest, and the recognition accuracy rate is 94.58%. The average recognition rate of feature training model with combination of the model with standard deviation, skeuteness and kurtosis is more than 90%. This method has high accuracy, robustness and good application value for the identification of primary cavitation of centrifugal pump.
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Received: 15 February 2020
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