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 …
Multivariate time-series prediction in industrial processes via a deep hybrid network under data uncertainty
With the rapid progress of the industrial Internet of Things (IIoT), reducing data uncertainty
has become a critical issue in predicting the development trends of systems and formulating …
has become a critical issue in predicting the development trends of systems and formulating …
How can applications of blockchain and artificial intelligence improve performance of Internet of Things?–A survey
In the era of the Internet of Things (IoT), massive computing devices surrounding us operate
and interact with each other to provide several significant services in industries, medical as …
and interact with each other to provide several significant services in industries, medical as …
Educational test-bed for Maintenance 4.0
M Diaz-Cacho, RL Cid, JM Acevedo… - 2022 IEEE Global …, 2022 - ieeexplore.ieee.org
This paper introduces a simple production and maintenance topology based on the Industry
4.0 principles. The paper defines the parts of the topology, hardware, software and …
4.0 principles. The paper defines the parts of the topology, hardware, software and …
Predictive Maintenance in Photovoltaic Systems Using Ensemble ML Empirical Analysis
This paper aims to enhance the effectiveness and sustainability of photovoltaic (PV) systems
by employing ensemble machine learning empirical analysis (EMLEA) to predict regular …
by employing ensemble machine learning empirical analysis (EMLEA) to predict regular …
Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets
K Al Abri, MS Sidhu - International journal of electrical and computer …, 2024 - hrcak.srce.hr
Sažetak The core aim of this study is to determine the most effective outlier detection
methodologies for multivariate psychological datasets, particularly those derived from Omani …
methodologies for multivariate psychological datasets, particularly those derived from Omani …
An Industrial and Public Power Grids Malfunction Detection Towards Data Driven
J Singh - 2023 3rd International Conference on Smart …, 2023 - ieeexplore.ieee.org
In this paper, we suggest exploring a concept for distant detection of faulty grid-supporting
equipment utilizing little data. These devices and their auxiliary functions, such reactive …
equipment utilizing little data. These devices and their auxiliary functions, such reactive …
Early Identification of Faults using Hybrid CNN Model for Industrial Internet of Things
A hybrid deep learning model will be used in the conducted study to prevent errors on the
Industrial Internet of Things for a thorough evaluation of defect, a deep convolution neural …
Industrial Internet of Things for a thorough evaluation of defect, a deep convolution neural …
A business process management model for predictive maintenance and remote monitoring of rural infrastructure supported by 4IR technologies
WB Richardson, J Meyer, S Von Solms - 2022 - ujcontent.uj.ac.za
People who reside in remote rural communities face a plethora of challenges on a day-to-
day basis. While benevolent donations in the form of equipment installations have been …
day basis. While benevolent donations in the form of equipment installations have been …
Data Management Framework for Resource Constrained IoT Applications
In recent years, the Internet of Things (IoT) has attracted industry-oriented researchers and
has become a common platform for most IoT-based applications. The increasing number of …
has become a common platform for most IoT-based applications. The increasing number of …