Development of Web-Based Monitoring Systems for Industrial Equipment Performance

Handoko Putra; Ratna Juwita  Fauziah; Karima  Aksa

doi:10.70716/reswara.v2i1.387

Authors

Handoko Putra Department of Industrial Engineering, Universitas Brawijaya, Malang, Indonesia Author
Ratna Juwita Fauziah Department of Information Systems, Telkom University, Bandung, Indonesia Author
Karima Aksa Department of Computer Science, University of Oran 1 Ahmed Ben Bella, Oran, Algeria Author

DOI:

https://doi.org/10.70716/reswara.v2i1.387

Keywords:

web-based monitoring, industrial equipment, performance monitoring, internet of things, predictive maintenance

Abstract

The rapid advancement of Industry 4.0 has significantly increased the demand for reliable and scalable monitoring systems to ensure optimal industrial equipment performance. Traditional monitoring approaches are often limited by isolated data acquisition, delayed diagnostics, and insufficient integration with web-based platforms. This study aims to develop and analyze a web-based monitoring system architecture for industrial equipment performance by synthesizing existing monitoring technologies, cloud-based infrastructures, Internet of Things (IoT), and predictive maintenance frameworks. The research adopts a qualitative systematic literature-based development approach by reviewing patents, journal articles, conference proceedings, and industrial case studies related to industrial equipment monitoring systems. The analysis focuses on system architecture, data acquisition mechanisms, communication protocols, dashboard visualization, and performance evaluation methods. The findings indicate that web-based monitoring systems significantly improve real-time visibility, centralized management, and decision-making efficiency. Integration with OPC UA, IoT sensors, cloud platforms, and edge computing enhances scalability, reduces maintenance costs, and enables predictive maintenance strategies. The study concludes that web-based monitoring systems represent a critical foundation for intelligent industrial operations and sustainable equipment management. The proposed conceptual architecture can serve as a reference model for future implementations in manufacturing, energy, and heavy machinery sectors.

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