Leak detection method of liquid-filled pipeline based on VMD and SVM

In order to solve the problem of inconspicuous leakage signal characteristics under external noise interference, a leakage detection method based on the combination of variational modal decomposition (VMD) and support vector machine (SVM) is proposed. The method first calculates the spearman correlation coefficients (SCC) of multiple intrinsic modal components (IMFs) obtained by VMD with the source signal, then extracts the energy and central frequency features of IMFs with larger SCC, and finally performs leak detection using the SVM classifier. The experimental results show that the VMD-SVM method can effectively perform leak detection with an accuracy of 98.27%. The accuracy of the VMD-SVM method proposed in this paper is improved by 6.5%, 5.63% and 10.39% compared to the time-frequency (TF) feature SVM, empirical modal decomposition (EMD) feature SVM and wavelet (DWT) feature SVM, methods, respectively. In addition, feature sensitivities are analyzed to reduce model complexity while ensuring accuracy.

Automated summary

To address the issue of inconspicuous leakage signal characteristics under external noise interference, a leakage detection method based on variational modal decomposition (VMD) and support vector machine (SVM) is proposed in this paper. The method calculates the spearman correlation coefficients (SCC) between multiple intrinsic modal components (IMFs) obtained by VMD and the source signal, extracts the energy and central frequency features of IMFs with larger SCC, and performs leak detection using the SVM classifier. Experimental results show that the VMD-SVM method achieves leak detection with an accuracy of 98.27%. Compared to other methods such as time-frequency (TF) feature SVM, empirical modal decomposition (EMD) feature SVM, and wavelet (DWT) feature SVM, the proposed VMD-SVM method improves the accuracy by 6.5%, 5.63%, and 10.39%, respectively. Moreover, feature sensitivities are analyzed to reduce model complexity while maintaining accuracy.