Valve internal leakage rate quantification based on factor analysis and wavelet-bp neural network using acoustic emission

Abstract

Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, the acoustic emission technique is characterized by nonintrusive and strong anti-interference ability, which can realize the in situ monitoring of the valve running status in real time. In this paper, acoustic emission signals from an internal leaking valve were obtained experimentally. Then, the dimensionality reduction technology based on factor analysis was introduced to the processing of valve internal leakage detection data. Next, the wavelet decomposition was carried out to decompose the sample feature set into four subsets. Finally, the decomposed sample feature sets were inputted into the error backpropagation (BP) neural network quantitative model, respectively. The optimized results show that the predicted internal leakage rate by the wavelet-BP neural network model has good precision with an error of less than 10%. The wavelet-BP neural network model can realize the analysis of the valve internal leakage rate quantitatively and has good robustness, which provides technical support and guarantees the safe operation of natural gas pipeline valves.