Implementation of Predictive Maintenance using IoT and Machine Learning for Smart Manufacturing Systems

DOI

10.2991/978-94-6463-926-1_34How to use a DOI?

Keywords

Condition Monitoring; Industrial Internet of Things (IIoT); Internet of Things (IoT); Machine Learning; Predictive Maintenance; Smart Manufacturing

Abstract

Predictive maintenance (PdM) has emerged as a critical strategy in smart manufacturing systems, aiming to reduce unplanned downtime, extend equipment lifespan, and improve overall operational efficiency. This paper presents the implementation of an integrated predictive maintenance framework that leverages Internet of Things (IoT) sensors and machine learning algorithms to monitor and predict equipment failures in real-time. The proposed system collects continuous data from industrial machines, such as vibration, temperature, and usage time, through a network of IoT devices. Machine learning models, including Random Forest and LSTM, are trained on historical maintenance records and sensor data to predict potential failures and generate early warnings. Experimental results from a simulated production environment demonstrate the effectiveness of the system, showing an accuracy of over 90% in failure prediction and a significant reduction in maintenance costs. This study highlights the potential of combining IoT and artificial intelligence for building intelligent, data-driven maintenance strategies aligned with Industry 4.0 principles.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Imam Sutrisno
AU  - Ari Wibawa
AU  - Urip Mudjiono
AU  - Ignatius Kristianto Agung Nugroho
AU  - Projek Priyonggo
AU  - Iskandar Iskandar
PY  - 2025
DA  - 2025/12/31
TI  - Implementation of Predictive Maintenance using IoT and Machine Learning for Smart Manufacturing Systems
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 297
EP  - 306
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_34
DO  - 10.2991/978-94-6463-926-1_34
ID  - Sutrisno2025
ER  -