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Simon Simarmata
Universitas Pamulang, Tangerang Selatan
Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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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 Adam Bahtiar Aditya Kusuma Wardana Aditya Prasetyo Agustinus Beda Buraq Agustya, Putra Dwi Ahmad Arifin Alfan Halyan Antoni Pribadi Anwar , La Ardy Wicaksono Ashari, Muhamad Aziz , Ficky Setyo Nur Aziz Nugroho Bambang Santoso Budi Artono Chikal Faqih Muharom Diyat Ilham Mustaqim Erland Radithya Putra Fabrianto, Luky Faizah, Novianti Madhona Fauzi , M. Ari Ghozi , Muhammad Fariz Hariyono, Muhammad Akbar Ikhsan , Ilham Nurul Ilham Syahgani Jamaluddin Jamaludin JAMALUDIN Jamaludin Jamaludin Karo Karo, Panser Lintar Yan Zuhara Lucky Yan Zuhara M. Risky Hidayatullah Maulana, Aldi Hikmah Meiton Boru Mohamad Taufik Wibowo Muhammad Hizkil Ramadhan Muhammad Raihan Syahputra Nico Carnando Hermawan Nur Rofiq Panser Karo karo Panser Karo-Karo Primadillah , Rangga Putro , Dwi Risqi Tristiyanto Rahmat Hidayat Rama Achmad Fadillah Rama Nittia Khadifa Ramadhan , Abdul Ridho Ramadhan, Ilham Ganang Rendy Wijaya Rizky Andriyanto Robi Farhan Anshori Rohmat Iktafal ma’arif Saputra, Muhammad Aziz Shita Nurul Ayasha Silvi Fitriya Sya'bana, Muhamad Aditya Syaeful Machfud Syafiq Al Ghifari Tedi Purwanto Tedi Purwanto Yeno Wenda Yodi Dwi Cahyo