[PDF][PDF] A comprehensive review of artificial intelligence and machine learning applications in energy sector

A Raihan - Journal of Technology Innovations and Energy, 2023 - researchgate.net
The energy industry worldwide is today confronted with several challenges, including
heightened levels of consumption and inefficiency, volatile patterns in demand and supply …

Recent progress on dynamics and control of pipes conveying fluid

Y Tang, HJ Zhang, LQ Chen, Q Ding, Q Gao… - Nonlinear Dynamics, 2024 - Springer
Pipeline systems are crucial in fluid-conveying pipes across diverse engineering disciplines,
including aerospace, oil transportation, deep-sea exploration, and nuclear energy projects …

Hydrogenerator early fault detection: Sparse dictionary learning jointly with the variational autoencoder

R Zemouri, R Ibrahim, A Tahan - Engineering Applications of Artificial …, 2023 - Elsevier
Monitoring the continuous health status of a Hydraulic Turbine Generator Unit (HTGU) is a
strategic task to prevent any unexpected downtime. In addition to the loss of energy …

An In-Depth Study of Vibration Sensors for Condition Monitoring

IU Hassan, K Panduru, J Walsh - Sensors, 2024 - mdpi.com
Heavy machinery allows for the efficient, precise, and safe management of large-scale
operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures …

Enhancing active noise control of road noise using deep neural network to update secondary path estimate in real time

JY Oh, HW Jung, MH Lee, KH Lee, YJ Kang - Mechanical Systems and …, 2024 - Elsevier
The performance of active noise control (ANC) is significantly influenced by the accuracy of
the secondary path estimate. In the case of a vehicle, dynamic environments, which …

Convergence and error analysis of PINNs

N Doumèche, G Biau, C Boyer - arxiv preprint arxiv:2305.01240, 2023 - arxiv.org
Physics-informed neural networks (PINNs) are a promising approach that combines the
power of neural networks with the interpretability of physical modeling. PINNs have shown …

Machine learning (ML) algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones

M Zain, U Dackermann, L Prasittisopin - Structures, 2024 - Elsevier
Ensuring seismic resilience of school buildings is crucial for safeguarding their occupants
during earthquakes. This paper focuses on assessing the seismic vulnerability of school …

Application of physics-informed machine learning for excavator working resistance modeling

S Li, S Wang, X Chen, G Zhou, B Wu, L Hou - Mechanical Systems and …, 2024 - Elsevier
Accurate measurement of the working resistance encountered during excavation plays a
vital role in improving production efficiency, reducing energy consumption, and enabling …

Deep learning uncertainty quantification for ultrasonic damage identification in composite structures

H Lu, S Cantero-Chinchilla, X Yang, K Gryllias… - Composite …, 2024 - Elsevier
In this paper, three state-of-the-art deep learning uncertainty quantification (UQ) methods–
Flipout probabilistic convolutional neural network (CNN), deep ensemble probabilistic CNN …

[HTML][HTML] Hybrid physics-based and data-driven impact localisation for composite laminates

D **ao, Z Sharif-Khodaei, MH Aliabadi - International Journal of …, 2024 - Elsevier
The current challenges facing data-driven impact localisation methods primarily involve
accurately localising impacts occurring outside the training impact coverage area …