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All Journal International Journal of Electrical and Computer Engineering 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.
Co-Authors Abba Ari, Ado Adamou Aboubakar, Moussa Abran, Alain Moulla, Donatien Koulla Mulongo, Yves