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All Journal International Journal of Electrical and Computer Engineering Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Mnkandla, Ernest
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Computer Science & IT Electrical & Electronics Engineering Engineering

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PdM-FSA: predictive maintenance framework with fault severity awareness in Industry 4.0 using machine learning Moulla, Donatien Koulla; Mnkandla, Ernest; Aboubakar, Moussa; Abba Ari, Ado Adamou; Abran, Alain
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7211-7223

Abstract

Predictive maintenance contributes to Industry 4.0, as it enables a decrease in maintenance costs and downtime while aiming to increase production and return on investment. Despite the increasing utilization of machine learning techniques in predictive maintenance in industrial systems over the past few years, several challenges remain to be addressed in the implementation of ML, including the quality of the data collected, resource constraints, and equipment heterogeneity. This study proposes an adaptive framework for predictive maintenance in the context of Industry 4.0, specifically in internet of things (IoT) systems, using machine learning (ML) models. In particular, this study introduces PdM-FSA, a new framework based on an ensemble classifier that takes advantage of four widely adopted ML models in the predictive maintenance literature: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The performance evaluation results showed that the PdM-FSA framework can perform well for predictive maintenance according to the severity of equipment malfunctions in a smart factory. The results of this study provide significant knowledge to researchers and practitioners on predictive maintenance in the context of Industry 4.0. and enables the optimization of processes and improves productivity.
A Theoretical Artificial Intelligence Framework for Electricity Generation Life Cycle Mnkandla, Ernest
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3019

Abstract

The global economic growth heavily lies on the manufacturing sector. However, over the past decade, manufacturing sector in general has been facing a serious production downturn, hence, operating below its capacity mostly due to its inefficient and ineffective production system. Additionally, due to failure for manufacturing sector to constantly measure, develop and implement novel approaches in order to make sure it positions itself for economic growth as well as unlocking new opportunities within the ever-changing and complex worldwide market environment. However, ss of 2011, Industrial revolution 4.0 seems to have been a crucial factor in reshaping the sociological, economic, and technological landscape. Businesses associated with exposure to continuous digital transformation are able to capitalize on Industrial revolution 4.0 potential but are also compelled to deal with various impediments. Yet, research on the opportunities associated with the integration of Industry 4.0 in manufacturing industries from a holistic perspective is scarce. To fill this research gap, this study adopted the PRISMA approach to conduct a thorough investigation on the potential opportunities related to the adoption of industry 4.0 in the context of manufacturing businesses.
Application of Artificial Neural Network into Manufacturing Processes Mnkandla, Ernest; Mulongo, Yves
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3022

Abstract

The neural network model is an advanced and effective tool aims at simulating the manufacturing operations. An important number of researchers have utilized artificial neural network (ANN) to optimising multiple response metrics in manufacturing applications. In the majority of situations, the use of ANN enables the prediction of the mechanical and physical properties of manufacturing goods based on provided technical data. To this end, the deployment of ANN in manufacturing sector is tremendously significant in terms of cost and material resource savings. Thus, Artificial neural network as a key component regarding the optimization of the manufacturing processes.
Stakeholder Analysis for Enhancing Ethics in Software Development: A Scoping Review Marebane, Senyeki Milton; Mnkandla, Ernest
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1263

Abstract

Stakeholder analysis has become a crucial means for achieving software development ethical goals. This scoping review study aims to provide an overview of the extent to which stakeholder analysis has been applied to enhance ethics in software development. The study explores research studies that have been published by IEEE, ACM, Science Direct, AIS and Journal of Business Ethics. PRISMA-ScR was employed to achieve the objective of the study. Only six research studies published from 1999 to 2015 met the selection criteria for data extraction and analysis. The results show that the focus of stakeholder analysis is on moral impulse, risk identification, stakeholder interactions, stakeholder classification and impact level. Furthermore, stakeholder analysis for enhancing ethics in software development is prevalent to empower the voiceless stakeholders, risk management and stakeholder mapping and quantification for measuring stakeholder impact on projects. The analysis of the results reveals several research gaps such as unavailable empirical studies beyond 2015, concentration only on requirements engineering and lack of studies on stakeholder analysis on emergent technologies. The implications of this study point to the need for more guidance and expanded use of stakeholder analysis across the complete software process to benefit software teams and ongoing research to harness the potential of this theory on enhancing ethics in the emergent technologies.
Co-Authors Abba Ari, Ado Adamou Aboubakar, Moussa Abran, Alain Marebane, Senyeki Milton Moulla, Donatien Koulla Mulongo, Yves