ORCID verified
28 Sept 2025
Fault Detection and Localization in Electrical Transmission Lines Using ELM and LSTM Networks Based on MATLAB/Simulink Modeling and SimulationPublisher siteActive, last published 2026
In machine fault diagnosis techniques
Amos Mulenga
Nzung
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Scholar
Expert“Research focused on Machine Fault Diagnosis Techniques and adjacent fields.”
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6
Citations
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Pioneer
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01 · About the work
What I study
mos Mulenga Nzung is a researcher working on Machine Fault Diagnosis Techniques. A fuller summary will appear once they update their profile.
02 · Reach
Publication output and reach
Amos's work has been cited 6 times across 2 active years. Bars show publications per year; the line shows cumulative citation totals across publications first appearing in each year.
03 · Research focus
Topics
Machine Fault Diagnosis Techniques×3Machine Learning and ELM×3Mineral Processing and Grinding×2
04 · Where I am
Affiliations
Affiliation on published researchUniversity of Kinshasa, DR CongoDrawn from the institution listed on this researcher’s published work.
Research themes (UN SDGs)
05 · Selected work
Recent publications
7 works28 Sept 2025
Fault Detection and Localization in Photovoltaic Systems Using Extreme Learning Machine: Handling Short Circuits and Partial ShadingPublisher site28 May 20251cites
Artificial Dataset Generation and Bagging Ensemble Learning for Enhanced Fault Detection in Induction MotorsPublisher site7 Oct 20243cites
Generation of Artificial Datasets for Fault Detection in Induction Motors Using Extreme Learning MachinePublisher site7 Oct 20241cites
Towards a Physics Informed Neural Network Approach for Modeling and Prediction of Renewable Energy Production: Use Case of Solar CellsPublisher site28 Sept 20241cites
A Stacking Ensemble Technique for Mechanical Fault Detection in Induction MotorsPublisher site28 Sept 2024
Enhancing Downlink Throughput Prediction in 4G LTE Networks Using Ensemble Learning TechniquesPublisher siteInterested in this research?
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