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ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Mechatronics
Volume 10 - 2024 |
doi: 10.3389/fmech.2024.1383202
This article is part of the Research Topic Artificial Intelligence for Mechatronics View all articles
An Intelligent Predictive Maintenance System Based on Random Forest for Addressing Industrial Conveyor Belt Challenges
Provisionally accepted
MINGYU WU
1,2
Kai Woon Goh
2*
Kam Heng Chaw
3*
Ye Sheng Koh
2*
Marvin Dares
2*
Che Fai Yeong
2*
Eileen L. Su
2*
Holderbaum William
4
Yunhui Zhang
1*- 1 Jiaxing Vocational Technical College, Wutong, China
- 2 Universiti Teknologi Malaysia, Johor Bahru, Malaysia
- 3 MODU System (S) Pte Ltd, Klang, Malaysia
- 4 Manchester Metropolitan University, Manchester, North West England, United Kingdom
This study introduces a sophisticated Intelligent Predictive Maintenance System for industrial conveyor belts, powered by the Random Forest (RF) machine learning model. The RF model was evaluated against established models such as Logistic Regression, Neural Networks, Decision Trees, and Gradient Boosting, demonstrating superior performance. The model achieved 100% accuracy in classifying gearbox lubricant levels and sprocket conditions, highlighting its potential to address critical challenges in predictive maintenance, such as avoiding unexpected downtimes. However, further validation with larger datasets and varied operational environments is recommended to confirm robustness. This performance highlights its effectiveness in multiclass fault detection and overfitting mitigation, establishing a new standard in predictive maintenance technology. The system, enhanced by a comprehensive sensor array, not only adeptly captures but also intelligently analyzes critical operational data, providing proactive and data-driven insights for maintenance decision-making. This study not only affirms the dominant stature of the RF model in predictive analytics but also underscores its pivotal role in optimizing maintenance strategies, enhancing operational efficiency, and ensuring the reliability of conveyor systems in industrial settings
Keywords: Predictive maintenance, Conveyor Belt Systems, random forest, machine learning, Operational efficiency, comparative analysis, Algorithm Evaluation
Received: 07 Feb 2024; Accepted: 29 Oct 2024.
Copyright: © 2024 WU, Goh, Chaw, Koh, Dares, Yeong, Su, William and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Kai Woon Goh, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Kam Heng Chaw, MODU System (S) Pte Ltd, Klang, Malaysia
Ye Sheng Koh, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Marvin Dares, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Che Fai Yeong, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Eileen L. Su, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Yunhui Zhang, Jiaxing Vocational Technical College, Wutong, China
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