pipeline maintenance teams

Abstract: The intensifying water shortage in Algeria makes clear the need of novel, sustainable strategies to protect distribution networks. Current infrastructure struggles with frequent failures, particularly leaks stemming from corrosion or handling damage during transport and installation. High-density polyethylene (HDPE) has become a material of choice because it handles these challenges well; it is cost-efficient, flexible, and tough enough to withstand harsh environments.This work investigates the failure risks of HDPE pipelines manufactured by ITP Alg{é}rie -- Innovation Transformation Polym{è}re. Mechanical tests were performed on HDPE 100 to determine its fundamental properties, including burst tests under internal pressure and load/unload experiments aimed at establishing a material-specific damage law revealed the dominant degradation mechanisms that occur under different operational and environmental conditions. Based on these findings, a predictive numerical model was developed to simulate damage progression and anticipate potential failures in service. This study introduces a unique hybrid composite patch designed for the precise rehabilitation of compromised pipeline sections. It shows strong promise for rebuilding the mechanical integrity of pipes, which is vital for maintaining a safe and long-lasting drinking water supply.

Abstract: Cavitation diagnosis in industrial pipeline systems is challenging because acoustic responses vary substantially with operating conditions, while similar time-frequency patterns may correspond to different cavitation states. To address this issue, we propose a physical operating-condition-guided vision-language matching (POC-VLM) for cavitation diagnosis. POC-VLM first constructs physical operating-condition-aware semantic descriptions by explicitly incorporating sample-specific operating variables into candidate cavitation texts. A time-frequency-conditioned text adaptation module then dynamically aligns these physical semantics with the observed acoustic response, followed by global-local vision-language matching to capture both holistic and localized discriminative patterns. In parallel, a K-nearest neighbours-based non-parametric physical operating-condition prior estimates the physical plausibility of each candidate state and is fused with the vision-language semantic evidence at the decision level. Experiments on three real-world cavitation datasets show that POC-VLM consistently outperforms representative data-driven, multimodal and domain-specific diagnostic methods. Further analyses verify the complementary roles of vision-language semantic matching and physical operating-condition priors, while demonstrating robust performance under varying sampling rates, noise levels and observation durations.