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Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems

Farooq, Umer, Ademola, Moses and Shaalan, Abdu (2024) Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems. Electronics, 13 (2). pp. 1-16. ISSN 2079-9292

Item Type: Article


In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses. Although conventional preventive maintenance mechanisms like time-based maintenance, routine inspections, and manual data analysis provide a certain level of fault prevention, they are often reactive, time-consuming, and imprecise. On the other hand, machine learning algorithms can detect anomalies early, process vast amounts of data, continuously improve in almost real time, and, in turn, significantly enhance the efficiency of modern industrial systems. In this work, we compare different machine learning and deep learning techniques to optimise the predictive maintenance of ball bearing systems, which, in turn, will reduce the downtime and improve the efficiency of current and future industrial systems. For this purpose, we evaluate and compare classification algorithms like Logistic Regression and Support Vector Machine, as well as ensemble algorithms like Random Forest and Extreme Gradient Boost. We also explore and evaluate long short-term memory, which is a type of recurrent neural network. We assess and compare these models in terms of their accuracy, precision, recall, F1 scores, and computation requirement. Our comparison results indicate that Extreme Gradient Boost gives the best trade-off in terms of overall performance and computation time. For a dataset of 2155 vibration signals, Extreme Gradient Boost gives an accuracy of 96.61% while requiring a training time of only 0.76 s. Moreover, among the techniques that give an accuracy greater than 80%, Extreme Gradient Boost also gives the best accuracy-to-computation-time ratio.

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Uncontrolled Keywords: machine learning; deep learning; predictive maintenance; ball bearings; data analysis
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Depositing User: Umer Farooq


Item ID: 17316
Identification Number:
ISSN: 2079-9292
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Users with ORCIDS

ORCID for Umer Farooq: ORCID iD

Catalogue record

Date Deposited: 09 Feb 2024 14:35
Last Modified: 09 Feb 2024 14:45


Author: Umer Farooq ORCID iD
Author: Moses Ademola
Author: Abdu Shaalan

University Divisions

Faculty of Technology


Engineering > Electrical Engineering

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