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

Aug 5, 2026

Industry 4.0 onward requires earlier, more reliable fault detection for rotating equipment, where ball bearing failures can cause downtime, inefficient operations, and high maintenance costs. Traditional preventive maintenance such as time-based schedules, routine inspections, and manual analysis is often reactive and imprecise. This study compares machine learning and deep learning approaches for predictive maintenance by evaluating classification and ensemble models—Logistic Regression, Support Vector Machine, Random Forest, and Extreme Gradient Boost—along with an LSTM recurrent neural network, using metrics including accuracy, precision, recall, and F1.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Introduction

Background on ball bearings and failure modes

Fault inspection and diagnostic challenges

Related approaches (machine learning and deep learning)

Farooq , Um er, Ademola , Mos e s an d Sh a alan , Abdu ( 2 0 2 4) Comp ar ative Analysis of M a chine Learnin g Mod els for Pr e dictive M ainten an c e of Ball Be arin g System s . Electronics, 1 3 (2) . pp . 1-1 6 . ISSN 2 0 7 9-9 2 9 2 Downloa d e d from : [http :// sur e . sun d erl an d. ac . uk/id/ e print / 1 7 3 1 6 /](http :// sur e . sun d erl an d. ac . uk/id/ e print / 1 7 3 1 6 /) U s a g e g uid eli n e s Ple a s e r efer to th e u s a g e guid eline s at [http :// sur e . sun d erl an d . ac. uk/ policies. html](http :// sur e . sun d erl an d . ac. uk/ policies. html) or altern atively cont act [sur e @ sun d erlan d. ac . uk](sur e @ sun d erlan d. ac . uk). electronics Article Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems Umer Farooq ∗,†, Moses Ademola † and Abdu Shaalan † Citation: Farooq, U.; Ademola, M.; Shaalan, A. Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems. Electronics 2024, 13, 438. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)electronics13020438 Received: 7 December 2023 Revised: 13 January 2024 Accepted: 19 January 2024 Published: 21 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . School of Engineering, Faculty of Technology, University of Sunderland, Sunderland SR6 0DD, UK; [bi30nn@student.sunderland.ac.uk](bi30nn@student.sunderland.ac.uk) (M.A.); [abdu.shaalan@sunderland.ac.uk](abdu.shaalan@sunderland.ac.uk) (A.S.) * [Correspondence: umer.farooq@sunderland.ac.uk](Correspondence: umer.farooq@sunderland.ac.uk) † The authors contributed equally to the work. Abstract: 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. Keywords: machine learning; deep learning; predictive maintenance; ball bearings; data analysis 1. Introduction The study of ball bearings in rotating machines has evolved over time due t

FAQs

Why is predictive maintenance important for ball bearing systems?

Ball bearing failures can lead to plant downtime, inefficient operations, and significant maintenance expenses. Earlier anomaly detection reduces downtime and improves operational efficiency.

Which machine learning models are evaluated in the study?

The study evaluates Logistic Regression and Support Vector Machine, and also compares ensemble methods including Random Forest and Extreme Gradient Boost.

How does the study assess model performance?

Models are compared using accuracy, precision, recall, F1 scores, and computation requirements, including training time for a vibration-signal dataset.

Aug 5, 2026

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