Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems
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 maintena...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/2/438 |
| _version_ | 1797344169857908736 |
|---|---|
| author | Umer Farooq Moses Ademola Abdu Shaalan |
| author_facet | Umer Farooq Moses Ademola Abdu Shaalan |
| author_sort | Umer Farooq |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:58:28Z |
| format | Article |
| id | doaj.art-a8ae23cc744346a9b772d8f9df351ce6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T10:58:28Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a8ae23cc744346a9b772d8f9df351ce62024-01-26T16:15:06ZengMDPI AGElectronics2079-92922024-01-0113243810.3390/electronics13020438Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing SystemsUmer Farooq0Moses Ademola1Abdu Shaalan2School of Engineering, Faculty of Technology, University of Sunderland, Sunderland SR6 0DD, UKSchool of Engineering, Faculty of Technology, University of Sunderland, Sunderland SR6 0DD, UKSchool of Engineering, Faculty of Technology, University of Sunderland, Sunderland SR6 0DD, UKIn 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.https://www.mdpi.com/2079-9292/13/2/438machine learningdeep learningpredictive maintenanceball bearingsdata analysis |
| spellingShingle | Umer Farooq Moses Ademola Abdu Shaalan Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems Electronics machine learning deep learning predictive maintenance ball bearings data analysis |
| title | Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems |
| title_full | Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems |
| title_fullStr | Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems |
| title_full_unstemmed | Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems |
| title_short | Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems |
| title_sort | comparative analysis of machine learning models for predictive maintenance of ball bearing systems |
| topic | machine learning deep learning predictive maintenance ball bearings data analysis |
| url | https://www.mdpi.com/2079-9292/13/2/438 |
| work_keys_str_mv | AT umerfarooq comparativeanalysisofmachinelearningmodelsforpredictivemaintenanceofballbearingsystems AT mosesademola comparativeanalysisofmachinelearningmodelsforpredictivemaintenanceofballbearingsystems AT abdushaalan comparativeanalysisofmachinelearningmodelsforpredictivemaintenanceofballbearingsystems |