Implementation of IoT-Based Remote Monitoring for Naval Electrical Systems : Enhancing Reliability and Reducing Maintenance Costs

The increasing complexity of naval electrical systems and the demand for higher operational reliability necessitate a shift from traditional maintenance methods to data-driven predictive maintenance solutions. This research examines the effectiveness of IoT-based remote monitoring and diagnostic systems in improving system reliability, cost-efficiency, and operational sustainability in naval engineering. A qualitative-empirical approach was applied, utilizing expert interviews, structured surveys, and field observations to assess the impact of IoT-driven predictive maintenance. The findings indicate a significant reduction in system failures (92/100), maintenance costs (89/100), and operational downtime, demonstrating the effectiveness of real-time fault detection and automated diagnostics. Additionally, the study identifies challenges in infrastructure readiness (78/100) and cybersecurity concerns (82/100), which must be addressed for large-scale implementation. While the adoption rate (88/100) reflects strong industry support, skill readiness gaps (80/100) suggest a need for enhanced technical training in maritime education. The research concludes that IoT-based predictive maintenance offers a transformative approach to naval maintenance strategies, ensuring sustainability, efficiency, and enhanced fleet readiness in modern maritime operations.

Authors

Publication Details

Journal
International Journal of Mechanical, Electrical and Civil Engineering.
Published
2025-03-11
DOI
https://doi.org/10.61132/ijmecie.v2i2.257
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

Implementation of IoT-Based Remote Monitoring for Naval Electrical Systems : Enhancing Reliability and Reducing Maintenance Costs

Mohamad Ridwan, Mudakir Mudakir, Pargaulan Dwikora Simanjuntak, R. Herlan Guntor
1 links
International Journal of Mechanical, Electrical and Civil Engineering.
Machine Fault Diagnosis Techniques
article

Implementation of IoT-Based Remote Monitoring for Naval Electrical Systems : Enhancing Reliability and Reducing Maintenance Costs

Mohamad Ridwan, Mudakir Mudakir, Pargaulan Dwikora Simanjuntak, R. Herlan Guntor
article en

Abstract

The increasing complexity of naval electrical systems and the demand for higher operational reliability necessitate a shift from traditional maintenance methods to data-driven predictive maintenance solutions. This research examines the effectiveness of IoT-based remote monitoring and diagnostic systems in improving system reliability, cost-efficiency, and operational sustainability in naval engineering. A qualitative-empirical approach was applied, utilizing expert interviews, structured surveys, and field observations to assess the impact of IoT-driven predictive maintenance. The findings indicate a significant reduction in system failures (92/100), maintenance costs (89/100), and operational downtime, demonstrating the effectiveness of real-time fault detection and automated diagnostics. Additionally, the study identifies challenges in infrastructure readiness (78/100) and cybersecurity concerns (82/100), which must be addressed for large-scale implementation. While the adoption rate (88/100) reflects strong industry support, skill readiness gaps (80/100) suggest a need for enhanced technical training in maritime education. The research concludes that IoT-based predictive maintenance offers a transformative approach to naval maintenance strategies, ensuring sustainability, efficiency, and enhanced fleet readiness in modern maritime operations.

International Journal of Mechanical, Electrical and Civil Engineering.Vol. 2(2)
Openalex Percentile: Top 95%
Machine Fault Diagnosis Techniques
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Implementation of IoT-Based Remote Monitoring for Naval Electrical Systems : Enhancing Reliability and Reducing Maintenance Costs — Mohamad Ridwan, Mudakir Mudakir, et al. · International Journal of Mechanical, Electrical and Civil Engineering. (2025) | TGRS Research Map | TGRS