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Your search keyword '"Dares, Marvin"' showing total 6 results
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6 results on '"Dares, Marvin"'

1. Automated Guided Vehicle Robot Localization with Sensor Fusion

Author

Dares, Marvin, Goh, Kai Woon, Koh, Ye Sheng, Yeong, Che Fai, Su, Eileen L. M., Tan, Ping Hua, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, Zurada, Jacek M., editor, Gunjan, Vinit Kumar, editor, and Balasubramanian, Raman, editor

Published
2022
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2. An intelligent predictive maintenance system based on random forest for addressing industrial conveyor belt challenges.

Author

Wu, Mingyu, Goh, Kai Woon, Chaw, Kam Heng, Koh, Ye Sheng, Dares, Marvin, Yeong, Che Fai, Su, Eileen L. M., William, Holderbaum, and Zhang, Yunhui

Subjects
MACHINE learning, RANDOM forest algorithms, DECISION trees, INDUSTRIALISM, BELT conveyors, BOOSTING algorithms
Abstract

This study introduces a sophisticated intelligent predictive maintenance system for industrial conveyor belts powered by a random forest machine learning model. The random forest 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 for addressing critical challenges in predictive maintenance, such as avoiding unexpected downtime. 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 dominance of the random forest 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. [ABSTRACT FROM AUTHOR]

Published
2025
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3. Machine Learning Based Predictive Maintenance System for Industrial Chain Conveyor System

Author

Goh, Kai Woon, Chaw, Kam Heng, Yong, Jonathan C. E., Koh, Ye Sheng, Dares, Marvin, Su, Eileen L. M., Yeong, Che Fai, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Chew, Esyin, editor, P. P. Abdul Majeed, Anwar, editor, Liu, Pengcheng, editor, Platts, Jon, editor, Myung, Hyun, editor, Kim, Junmo, editor, and Kim, Jong-Hwan, editor

Published
2021
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4. Optimizing assembly processes with augmented reality: a case study on TurtleBots.

Author

Mingyu Wu, Ye Sheng Koh, Che Fai Yeong, Kai Woon Goh, Dares, Marvin, Eileen Su Lee Ming, Holderbaum, William, and Shahrizal Sunar, Mohd

Subjects
INDUSTRIAL robots, AUGMENTED reality, INDUSTRIAL applications, COMPARATIVE studies
Abstract

Augmented reality (AR) technology is revolutionizing traditional assembly processes, offering intuitive and interactive guidance that significantly enhances operational efficiency and accuracy. This study investigates the impact of AR on the assembly of Turtlebots, a complex task representative of industrial applications. Through a comparative analysis involving traditional paper manuals, modified paper manuals, and AR-based manuals, the benefits of AR integration are quantitatively assessed. Participants utilizing AR-based manuals completed the Turtlebot assembly 21.72% faster than those using traditional paper manuals, with a notable reduction in assembly time from an average of 03:00:40 to 02:21:26. Furthermore, the incidence of assembly errors significantly decreased, with AR manual users making an average of 2.25 errors compared to 5 by paper manual users. These findings underscore the potential of AR to expedite complex assembly tasks and enhance the accuracy of these processes. The study highlights the novel application of AR in improving both the speed and quality of assembly in an industrial context, demonstrating AR’s role as a pivotal technology for the future of manufacturing. [ABSTRACT FROM AUTHOR]

Published
2024
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