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Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration

Ahmad, Baseer; Mishra, Bhupesh Kumar; Ghufran, Muhammad; Pervez, Zeeshan; Ramzan, Naeem

Authors

Mr Baseer Ahmad Baseer.Ahmad@hull.ac.uk
Lecturer in Robotics and Artificial Intelligence

Bhupesh Kumar Mishra

Muhammad Ghufran

Zeeshan Pervez

Naeem Ramzan



Abstract

Machines have come a long way, from the industrial revolution to a modern-day industry 4.0. In this massive transition, one thing that has never changed within a machine is the moving part. Most industries use rotating machine with different load capacity and speed. These machines run at variable load and variable speed creating vibration bootstrap thus causing machine failure due to an increase in vibrations. Most of the researcher used vibration for fault detection in bearings but sometimes it caused by miss alignment in a shaft due to a fraction of overloading the machine. In this paper, we address it to solve those problems by using two parameters speed and vibration. To verify our approach, we use three different kinds of machine learning algorithms: Support Vector Machine (SVM), Naïve Bays, and Random Forest. By using these machine learning algorithms, we tried to find out the relationship between machine failure due to speed and vibration by predicting good and faulty bearings. After applying these models, we have seen that the SVM has 78% accuracy as compared to Naïve Bays, and Random Forest.

Citation

Ahmad, B., Mishra, B. K., Ghufran, M., Pervez, Z., & Ramzan, N. (2021). Intelligent Predictive Maintenance Model for Rolling Components of a Machine based on Speed and Vibration. In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (459-464). https://doi.org/10.1109/ICAIIC51459.2021.9415249

Conference Name 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021
Conference Location Jeju Island, Korea (South)
Start Date Apr 13, 2021
End Date Apr 16, 2021
Acceptance Date Dec 16, 2020
Online Publication Date Apr 29, 2021
Publication Date 2021
Deposit Date Nov 24, 2022
Publisher Institute of Electrical and Electronics Engineers
Pages 459-464
Book Title 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
ISBN 9781728176383
DOI https://doi.org/10.1109/ICAIIC51459.2021.9415249
Keywords Vibrations , Support vector machines , Shafts , Industries , Machine learning algorithms , Rotating machines , Forestry
Public URL https://hull-repository.worktribe.com/output/4131967
Publisher URL https://ieeexplore.ieee.org/document/9415249/

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