Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Robert Agyare Ofosu
Jiangsu University
Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Speed Control of an Electrical Cable Extrusion Process Using Artificial Intelligence-Based Technique Robert Agyare Ofosu; Erwin Normanyo; N-Yo Abdul-Aziz; Stephen Smart Stickings
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1045.2023

Abstract

Most cable manufacturing companies use Programmable Logic Controllers with conventional controllers to control line speed during cable extrusion. These traditional controllers have difficulties keeping the line speed constant, causing surface defects on the extruded cables and affecting the quality of the manufactured cables. To overcome these challenges, data on the causes of defects during cable manufacturing were collected from a cable manufacturing company in Ghana to ascertain the possible causes during cable manufacturing. Adaptive Neuro-Fuzzy Inference System (ANFIS) controller was designed to provide a constant line speed during the cable extrusion process. To ascertain its robustness, the ANFIS controller was compared to a conventional Proportional Integral Derivative controller and a Fuzzy Logic controller. The controllers were designed and simulated using MATLAB/Simulink software. The analysis of the collected data indicated that a break in insulation/ sheath was a frequently occurring defect during the cable manufacturing process due to improper line speed control of the machines used in the cable manufacturing process. Based on the results obtained from the various controllers, it was concluded that the ANFIS controller was robust in achieving stability regarding line speed variations.
Fault Detection and Diagnosis of a 3-Phase Induction Motor Using Kohonen Self-Organising Map Robert Agyare Ofosu; Benjamin Odoi; Daniel Fosu Boateng; Asaph Mbugua Muhia
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1047.2023

Abstract

This paper uses the Kohonen Self-Organising Map (KSOM) to detect, diagnose, and classify induction motor faults. A series of simulations using models of the 3-phase induction motor based on real industrial motor parameters were performed using MATLAB/Simulink under fault conditions such as inter-turn, power frequency variation, over-voltage and unbalance in supply voltage. The model was trained using the input signals of the various fault conditions. Various faults from an unseen induction motor were fed to the model to test the model’s ability to detect and classify induction motor faults. The KSOM adapted to the conditions of the unseen motor, detected, diagnosed and classified these faults with an accuracy of 94.12%.
Co-Authors Asaph Mbugua Muhia Benjamin Odoi Daniel Fosu Boateng Erwin Normanyo N-Yo Abdul-Aziz Stephen Smart Stickings