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.

Offshore Wind Power Foundation Corrosion Rate Prediction Model Based on Improved SHO Algorithm

“…For small sample data, the support vector machine fitting effect is better; the literature [15] uses the artificial bee colony algorithm to optimize the SVM parameters, the establishment of the grounding network corrosion rate prediction model, and corrosion test data to verify the practicality of the model. H. Zhao, Z. Li, S. Zhu, and Y. Yu used the extended memory factor to establish a grounding network corrosion prediction model [16]. However, the optimization algorithm has high requirements for data due to its complex structure, and the parameters of the SVM model are difficult to determine to achieve the expected results, and the algorithm has to be optimized [17,18].…”

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

“…Prior to identification, it is typically necessary to preprocess the signals using mathematical methods to extract key features [12]. Wavelet transform is capable of capturing the transient characteristics of signals but requires appropriate selection of wavelet basis functions and scales [13]. Fourier transform is widely used for frequency domain analysis of acoustic emission signals but is less effective in capturing transient events compared to wavelet transform [14].…”

Identification of the Major Noise Energy Sources in Rail Vehicles Moving at a Speed of 200 km/h

“…It is worth mentioning that the occurrence of noise, or acoustic emission, can also bring many benefits. Acoustic emission analysis is used in many sectors of transport and industry, among others, to detect and monitor damages, leaks, fatigue, or structural analysis of various materials (e.g., concrete, plastics, wood, ceramics) [6][7][8][9][10][11][12].…”