Pipes play a crucial role in transporting essential resources such as water, oil, and gas across industrial, urban, and environmental infrastructures. Monitoring of the flow capacity in such extended structures has is a still persisting issue, potentially resulting in operational disruptions and serious safety risks. The current demand on these systems' reliability have been increasing maintenance costs. Therefore, any advance in diagnosis methods and tools is benefic. This study presents a prognostic and health monitoring approach that utilizes flow-induced acoustic emissions to detect and characterize pipeline blockages. An analytical model and a finite element is developed to capture the acoustic signatures flow-induced disturbances on the structure, and how the acoustic wave propagation is affected by the level of clogging. This reveals features highly sensitive to the changes of the flow rate. These insights drive the development of a machine learning-based predictive maintenance strategy, validated on real-case datasets. The results demonstrate exceptional accuracy, with most classifiers achieving 100% detection rates for clogging presence, shape, and severity. Additionally, model generalization tests show that machine learning algorithms adapt more effectively to varying clogging thickness than clogging shape. This research is the first step for more enhancing predictive maintenance and ensuring the reliability of industrial pipelines.
Preserving the structural integrity of heritage masonry arch bridges presents unique challenges, particularly within historically dense environments like Venice where non-invasive methods are paramount. Ambient vibration monitoring (AVM) offers a well-established starting point,…
TL;DR: A framework for CVSHM, which allows for robust detection, localization, and assessment of the damage even for highly contaminated displacement data, is proposed and tested using realistic synthetic videos representing vibrating truss structure.
Computer-vision-based structural health monitoring (CVSHM) enables contactless displacement measurement at multiple locations on the vibrating structure. Additionally, such a measurement can be realized from a certain distance from the monitored infrastructure. It provides a poss…
TL;DR: This article focuses on the radar-only computer-vision task of classifying rotors without complementary costly instrumentation, and measured radargrams from field experiments are complemented with the novel synthetic dataset SiWiRoRa as well as further open imagery.
Tower-radar computer vision (TRCV) represents an emerging application-oriented field of study. Here, image-type measurements are acquired from radar transceivers bound to the mast of wind power turbines and these radargrams subsequently get analyzed using data-driven algorithms.…
The safe and reliable operation of natural gas compressor units is crucial for ensuring a secure and stable gas supply. However, the interaction between natural gas and pipelines inevitably induces flow-induced vibrations, leading to long-term cyclic stress variations in the pipe…
Modal filtering transforms spatial vibration measurements into modal coordinates, simplifying tasks such as model correlation, force identification, and damage detection. Classical modal filters rely on a full modal model consisting of natural frequencies, damping ratios, and mod…
TL;DR: This study investigates and compares three different algorithms for real-time displacement extraction, evaluating their performance under controlled conditions through synthetic video sequences with exactly known imposed motion, and identifies their strengths and limitations.
Vision-based displacement measurement has gained considerable attention in Structural Health Monitoring (SHM), where the need to detect small structural motions with non-contact instrumentation has driven the development of subpixel estimation algorithms capable of achieving reso…