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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Published by Universitas Ahmad Dahlan
ISSN : 23383070     EISSN : 23383062     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
JITEKI (Jurnal Ilmiah Teknik Elektro Komputer dan Informatika) is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD) in collaboration with Institute of Advanced Engineering and Science (IAES). The aim of this journal scope is 1) Control and Automation, 2) Electrical (power), 3) Signal Processing, 4) Computing and Informatics, generally or on specific issues, etc.
Arjuna Subject : -
Articles 1 Documents
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Search results for , issue "Vol. 12 No. 1 (2026): March" : 1 Documents clear
Success Factors in the Implementation of IoT-Enabled Predictive Maintenance Technology in Industrial Electrical Applications: A Systematic Literature Review Maringga, Daniel Ngolu Jiledo; Ali, Muhamad; Azahar, Ridho
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.31651

Abstract

The development of the Internet of Things (IoT) in predictive maintenance (IoT-enabled Predictive Maintenance) for industrial electrical equipment offers significant potential to enhance system efficiency and reliability; however, its implementation is constrained by challenges related to sensor data integration, communication infrastructure quality, and security issues. This study addresses a gap in the literature by describing patterns of successful IoT-enabled Predictive Maintenance implementation in industrial electrical applications. The contribution of this research lies in providing a systematic synthesis of leading technologies and key success factors in the adoption of IoT-enabled Predictive Maintenance.  The method employed is a Systematic Literature Review (SLR) using the PRISMA approach, which resulted in 16 relevant articles. The findings indicate that the combination of IoT technologies, sensors, wireless networks, and edge-cloud architecture represents an appropriate technological configuration for building an effective Predictive Maintenance chain. These implementations are predominantly found in the manufacturing, energy, and transportation sectors, with the main success factors determined by data quality and network sustainability. These findings offer practical solutions for industry practitioners in improving the efficiency and sustainability of their systems. In conclusion, the successful implementation of IoT-enabled Predictive Maintenance in industrial electrical systems is highly dependent on the suitability of technological infrastructure, data governance, and service-based business models, while also opening opportunities for further research and the expansion of applications into other sectors.

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