Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects
MY Arafat, MJ Hossain, MM Alam - Renewable and Sustainable Energy …, 2024 - Elsevier
Predictive maintenance is an essential aspect of microgrid operations as it enables
identifying potential equipment failures in advance, reducing downtime, and increasing the …
identifying potential equipment failures in advance, reducing downtime, and increasing the …
A review of distributed acoustic sensing applications for railroad condition monitoring
Accurate condition monitoring has been a major challenge among railroad management
authorities as they work to minimize collisions that lead to fatalities or damage to railroads …
authorities as they work to minimize collisions that lead to fatalities or damage to railroads …
Investigating photovoltaic solar power output forecasting using machine learning algorithms
Solar power integration in electrical grids is complicated due to dependence on volatile
weather conditions. To address this issue, continuous research and development is required …
weather conditions. To address this issue, continuous research and development is required …
Optimized EWT-Seq2Seq-LSTM with attention mechanism to insulators fault prediction
Insulators installed outdoors are vulnerable to the accumulation of contaminants on their
surface, which raise their conductivity and increase leakage current until a flashover occurs …
surface, which raise their conductivity and increase leakage current until a flashover occurs …
Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due
to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly …
to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly …
Toward intelligent food drying: Integrating artificial intelligence into drying systems
SH Miraei Ashtiani, A Martynenko - Drying Technology, 2024 - Taylor & Francis
Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly
evolving disciplines with increasing applications in modeling, simulation, control, and …
evolving disciplines with increasing applications in modeling, simulation, control, and …
Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
High loads of suspended sediments in rivers are known to cause detrimental effects to
potable water sources, river water quality, irrigation activities, and dam or reservoir …
potable water sources, river water quality, irrigation activities, and dam or reservoir …
A multihead LSTM technique for prognostic prediction of soil moisture
P Datta, SA Faroughi - Geoderma, 2023 - Elsevier
Prognostic prediction of soil moisture is a critical step in various fields such as geotechnical
engineering, agriculture, geology, hydrology, and climatology. For example, in agricultural …
engineering, agriculture, geology, hydrology, and climatology. For example, in agricultural …
Early predicting tribocorrosion rate of dental implant titanium materials using random forest machine learning models
Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may
lead to secondary revision surgery and associated risks of implantable devices. Therefore …
lead to secondary revision surgery and associated risks of implantable devices. Therefore …
LSTM-GAN-AE: A promising approach for fault diagnosis in machine health monitoring
H Liu, H Zhao, J Wang, S Yuan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Recent years have witnessed that real-time health monitoring for machine gains more and
more importance with the goal of achieving fault diagnosis (FD) and predictive maintenance …
more importance with the goal of achieving fault diagnosis (FD) and predictive maintenance …