Classification modeling of valve internal leakage acoustic emission signals based on optimal wavelet scattering coefficients.

[Display omitted] • An experimental setup was built up to collect the VILAES. • The wavelet scattering coefficients are selected as the features of the VILAES. • Relationship between scattering coefficients and leakage rate is determined. • Optimal wavelet scattering coefficients are extracted automatically. • A new multi-variable classification model for small leakage and small sample sizes. In order to achieve acoustic emission detection in valve internal leakage, it is essential to extract features, and establish an accurate mathematical model. Current valve internal leakage acoustic emission signal (VILAES) classification models mostly rely on human experience for selecting features, resulting in low accuracy for small leakage conditions. This paper combines the wavelet scattering transform (WST), Relief-F algorithm and AdaBoost.M1 algorithm, and proposes to use the optimal wavelet scattering coefficients as features to establish the VILAES classification model accurately for small leakage. Firstly, the first three order wavelet scattering coefficients of the VILAES are automatically extracted using WST and are transformed into one-dimensional features. Secondly, the Relief-F algorithm is employed to select the optimal feature subset. Finally, the optimal wavelet scattering coefficients and pressures are used as inputs to establish a classification model for VILAES and achieve an accuracy over 96.80% for small leakage. [ABSTRACT FROM AUTHOR]