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Hariyono, Muhammad Akbar
Department Of Medical Electronic Engineering Technology, Politeknik Unggulan Kalimantan, Banjarmasin Utara, Kalimantan Selatan, Indonesia
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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
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.407

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.
Co-Authors A Halim Achmadi achmadi achmadi Ahmad Faqih Habibi Ahmad Robittah Al Farizi, Hafiz Aldinor Setiawan Aldinor Setiawan Anshari, Ridho Antoni Pribadi Ardy Wicaksono Arief Marwanto Ariyani, Gina Aulia Rahman Bayu Setyo Wibowo Bayu Setyo Wibowo Budi Artono Donny Martha Farizi, Hafiz Al Fatimah Fatimah Galih Persadha Gutrio Habibi, Ahmad Faqih Hafiz Al Farizi Hazizah, Hazizah Hikmah Maya Sari Hikmah Maya Sari Isna Isna JAPERI JAPERI Kusnadi Kusnadi KUSUMA WARDANI, IKA Maya Sari, Hikmah Muhammad Alpian Hadi Muhammad Maulana Akbar Muhammad Zaini Muhammad Zaini Muhammad Zaini Panser Karo-Karo Persadha, Galih Priyadi, Budi Rakhmad Rizkiansyah Rizkiansyah, Rakhmad Robittah, Ahmad Rusdiansyah Rusdiansyah Sabitah, A'yan Santoso, Blegoh Iwan Saputra, M Risky Setiawan, Aldinor Shaufatul Abdaniah Simon Simarmata Suryani Alifah Syauqi, Muhammad Fuad Syukur Yakub Tahdi, Muhammad Tengku Riza Zarzani N Upik Ari Erlita Upik Ari Erlita Widyawati, Tri Aksari Yuliana Salman Yuliana Salman Zulfa ‘Izzatun Nisa