A study on valve internal leakage identification and leakage rate quantification

Valves are a vital component in natural gas pipelines. If internal leakage occurs, it will bring economic losses and potential production safety hazards. Therefore, the effective diagnosis of valve internal leakage and accurate quantification of internal leakage rate are of great significance. Aiming at the problem of low efficiency of internal leakage diagnosis under complex background noise, based on the power spectral density of internal leakage acoustic signals and non-leakage noise signals, convolutional neural network (CNN) identification models of valve internal leakage were proposed. Aiming at the problem of large quantization error of physical theory and shallow network models in multi-conditions internal leakage data-sets, the deep belief network(DBN) regression model of valve internal leakage rate was proposed, and compared with traditional models such as support vector regression and back propagation neural network. The results show that the valve internal leakage diagnosis accuracy is 99% and the mean absolute percentage error(MAPE) of internal leakage rate quantification is 9.101 2, which proves the efficiency of the proposed models. © 2022, Editorial Office of Journal of Vibration and Shock. All right reserved.