Pipeline leak diagnosis using multisource multiscale attention entropy and enhanced least square twin support vector machine

Abstract To achieve rapid and precise identification of water supply pipeline leakage faults, this study introduces a diagnostic framework that integrates multisource multiscale attention entropy (MMATE), an enhanced least-squares twin support vector machine (ELSTSVM), and an adaptive boosting (AdaBoost) algorithm. The MMATE-ELSTSVM-AdaBoost architecture follows a three-stage workflow. First, an improved wavelet threshold denoising (IWTD) technique featuring a novel threshold-processing function is developed to suppress signal noise. Second, MATE values are extracted from the denoised signals, and a multidimensional feature vector is constructed by integrating MATE characteristics across multisource sensors. Third, the ELSTSVM’s objective function is enhanced through a reformulated distance metric, augmented with regularization and compactness terms to optimize model robustness and generalizability. The refined MMATE feature vectors are then fed into the ELSTSVM-AdaBoost ensemble for fault classification. Experimental evaluations demonstrate that the proposed framework significantly outperforms conventional single-feature approaches, standalone models, and state-of-the-art SVM variants, achieving a peak classification accuracy of 99.10% in leakage detection tasks.