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All Journal International Journal of Electrical and Computer Engineering The Indonesian Journal of Computer Science
Moulla, Donatien Koulla
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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.
Using Quality Measures During the Software Development Process: Case Study of Cameroonian Software Industry Oumarou, Hayatou; Moulla, Donatien Koulla; Kolyang, kolyang
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): 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.v12i3.3208

Abstract

Many studies on software quality use a variety of techniques and tools to assess quality in IT organizations. However, it is still difficult to ensure the proper use of measures to guarantee software quality. Cameroon, like many developing countries, faces a number of challenges in its software industry including limited market size, poor infrastructure, and lack of software engineering best practices. This study evaluates the software quality measurement practices in Cameroon and identifies potential areas of improvement. This study conducted a questionnaire survey of 30 companies by identifying five main categories and nine research questions. 57% of the companies surveyed consider that the impact of the measures on the success of the project is significant, and the measurement findings are, by large, accessible to executives as well as to the staff concerned. Furthermore, the adoption of a measurement tool can improve the monitoring and management of software projects.
Co-Authors Abba Ari, Ado Adamou Aboubakar, Moussa Abran, Alain Kolyang, Kolyang Mnkandla, Ernest Oumarou, Hayatou