Deep learning for structural health monitoring: Data, algorithms, applications, challenges, and trends
J Jia, Y Li - Sensors, 2023 - mdpi.com
Environmental effects may lead to cracking, stiffness loss, brace damage, and other
damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) …
damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) …
Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems
A challenging problem in risk and reliability analysis of Complex Engineering Systems
(CES) is performing and updating risk and reliability assessments on the whole system with …
(CES) is performing and updating risk and reliability assessments on the whole system with …
Detection of the pipeline elbow erosion by percussion and deep learning
Elbows are commonly used in pipelines to change the direction of flow, and the pipeline
elbows are prone to erosion caused by the transported medium. Detection of the pipeline …
elbows are prone to erosion caused by the transported medium. Detection of the pipeline …
From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods
Owing to recent advancements in sensor technology, data mining, Machine Learning (ML)
and cloud computation, Structural Health Monitoring (SHM) based on a data-driven …
and cloud computation, Structural Health Monitoring (SHM) based on a data-driven …
Towards interpretable deep learning: a feature selection framework for prognostics and health management using deep neural networks
In the last five years, the inclusion of Deep Learning algorithms in prognostics and health
management (PHM) has led to a performance increase in diagnostics, prognostics, and …
management (PHM) has led to a performance increase in diagnostics, prognostics, and …
Real-time determination of elastic constants of composites via ultrasonic guided waves and deep learning
S Wang, Z Luo, J Jing, Z Su, X Wu, Z Ni, H Zhang - Measurement, 2022 - Elsevier
An immediate and convenient report of mechanical properties of composites with full
automation is crucial for timely characterizing the time-dependent degradation of material …
automation is crucial for timely characterizing the time-dependent degradation of material …
[HTML][HTML] Characterization of signature trends across the spectrum of non-alcoholic fatty liver disease using deep learning method
I Park, N Kim, S Lee, K Park, MY Son, HS Cho, DS Kim - Life Sciences, 2023 - Elsevier
Aims The timely diagnosis of different stages in NAFLD is crucial for disease treatment and
reversal. We used hepatocellular ballooning to determine different NAFLD stages. Main …
reversal. We used hepatocellular ballooning to determine different NAFLD stages. Main …
[HTML][HTML] Exploring Quantum Machine Learning and feature reduction techniques for wind turbine pitch fault detection
Driven by the development of machine learning (ML) and deep learning techniques,
prognostics and health management (PHM) has become a key aspect of reliability …
prognostics and health management (PHM) has become a key aspect of reliability …
Review on deep learning in structural health monitoring
Road bridges are fundamental and most critical elements of land transportation routes which
allow to overpass many physical obstacles. Therefore, these elements have to be preserved …
allow to overpass many physical obstacles. Therefore, these elements have to be preserved …
Deep learning health state prognostics of physical assets in the Oil and Gas industry
J Figueroa Barraza… - Proceedings of the …, 2022 - journals.sagepub.com
Due to its capital-intensive nature, the Oil and Gas industry requires high operational
standards to meet safety and environmental requirements, while maintaining economical …
standards to meet safety and environmental requirements, while maintaining economical …