Design of an Edge Computing Based Industrial Internet of Things Architecture for Real Time Predictive Maintenance in Advanced Manufacturing Systems

Simon Simarmata; Panser Karo-Karo; Budi Artono; Muhammad Akbar Hariyono; Ardy Wicaksono; Antoni Pribadi

doi:10.61132/ijmicse.v2i4.407

Authors

Simon Simarmata Universitas Pamulang
Panser Karo-Karo Universitas Tamajagakarsa
Budi Artono Politeknik Negeri Madiun
Muhammad Akbar Hariyono Politeknik Unggulan Kalimantan
Ardy Wicaksono Universitas Sugeng Hartono
Antoni Pribadi Politeknik Kampar

DOI:

https://doi.org/10.61132/ijmicse.v2i4.407

Keywords:

Anomaly Detection, Edge Computing, IIoT Systems, Machine Monitoring, Predictive Maintenance

Abstract

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

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