Offshore Platform Pipeline Leakage Valve Localization Using DCEEMDAN and ATSFN

作者：Yuchen Lu, Menghan Chen, Xiaolong Qiu, Weizhe Ren, Chuanyang Zhao, Hongbing Liu · 发表于：Journal of Offshore Mechanics and Arctic Engineering · 年份：2025 · DOI：10.1115/1.4069875 · 被引用次数：11 · 研究领域：Advanced Sensor and Control Systems、Network Security and Intrusion Detection

Abstract Offshore platform pipeline leakage detection faces severe challenges from complex marine environments, where intense environmental noise interference and complex signal characteristics make traditional methods difficult to achieve accurate leakage valve localization. To address this technical challenge, this study proposes an offshore platform pipeline leakage valve localization method based on dynamic time warping distance-based complete ensemble empirical mode decomposition with adaptive noise (DCEEMDAN) and adaptive temporal–spatial fusion network (ATSFN). First, by introducing dynamic time warping distance similarity measurement into the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) framework and combining probability density function feature extraction, adaptive denoising of acoustic emission signals in marine environments is achieved. Second, a temporal–spatial feature extraction architecture with a parallel multiscale convolutional neural network (CNN) and a hierarchical GRU is designed, realizing deep fusion of CNN spatial features and GRU temporal features through a cross-attention mechanism. Finally, an end-to-end intelligent monitoring system is constructed, achieving high-precision localization of 10 valve positions through dual-stage verification combining laboratory experiments and offshore platform field measurements. Experimental results show that DCEEMDAN outperforms traditional EMD series algorithms, achieving a signal...