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Remaining Useful Life Prediction for Rolling Element Bearings
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Remaining Useful Life Prediction for Rolling Element Bearings

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 12757

Special Issue Editor

Dr. David Isaac Ibarra-Zarate

E-Mail Website
Guest Editor
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, NL, Mexico
Interests: acoustics; acoustics and acoustic engineering; noise analysis; acoustic signal processing; acoustic analysis; wave propagation; vibration analysis; signal processing; audio signal processing; audio engineering

Special Issue Information

Dear Colleagues,

Industry 4.0 has transformed the business environment, from the increase in sensors and control systems to the development of new maintenance strategies, with data-based decision making being one of the most popular aspects. This type of maintenance aims to optimize the times of activities and is more efficient from an economic point of view compared to traditional methods, based on the times and similarities. Data-based maintenance increases system reliability by improving the useful life of machine components, allowing them to be repaired or replaced before a critical failure occurs that causes severe and costly problems. The most vulnerable components of rotating machines are bearings, and they are widely used in the manufacturing industry; most of the critical failures in industrial machinery occur due to the malfunction of these elements, to such an extent that by guaranteeing the correct operation of the bearings a safe and economical state of operation is generated during the production process. Bearings are one of the main sources of nonlinearity in systems formed by rotating machines, since they significantly affect their operation. This nonlinear behavior has led to the development of a wide range of techniques, both for monitoring and for maintenance, making it possible to guarantee the normal operation of a machine's bearings. In applications such as turbines and aircraft engines, the condition of these elements is paramount because a simple imperfection can cause critical problems and extremely dangerous as well as expensive results. In CNC machine tools, the progressive wear of bearings cannot be avoided and it is important to continuously monitor and diagnose in order to generate an accurate diagnosis of the condition of machinery.

Dr. David Isaac Ibarra-Zarate
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault diagnostics
  • rolling element bearing
  • remaining useful life prediction
  • condition monitoring

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Published Papers (5 papers)

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Research

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18 pages, 3422 KiB  
Article
Use of Image Recognition and Machine Learning for the Automatic and Objective Evaluation of Standstill Marks on Rolling Bearings
by Markus Grebe, Alexander Baral and Dominik Martin
Machines 2024, 12(12), 840; https://doi.org/10.3390/machines12120840 - 23 Nov 2024
Viewed by 750
Abstract
One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into [...] Read more.
One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into six classes. An internal project has now investigated the extent to which this evaluation can be automated and objectified. Images of standstill marks were classified using convolutional neural networks that were implemented with the deep learning library Pytorch. With basic convolutional neural networks, an accuracy of 70.19% for the classification of all six classes and 83.65% for the classification of pairwise classes was achieved. Classification accuracies were improved by image augmentation and transfer learning with pre-trained convolutional neural networks. Overall, an accuracy of 83.65% for the classification of all six standstill mark classes and 91.35% for the classification of pairwise classes was achieved. Since 16 individual marks are generated per test run in a typical quasi standstill test (QSST) of the CCT and the deviation in the prediction of the classification is a maximum of one school grade, the accuracy achieved is already sufficient to carry out a reliable and objective evaluation of the markings. Full article
(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)
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21 pages, 4865 KiB  
Article
Digital Twin-Driven Remaining Useful Life Prediction for Rolling Element Bearing
by Quanbo Lu and Mei Li
Machines 2023, 11(7), 678; https://doi.org/10.3390/machines11070678 - 24 Jun 2023
Cited by 9 | Viewed by 2732
Abstract
Traditional methods for predicting remaining useful life (RUL) ignore the correlation between physical world data and virtual world data, leading to the low prediction accuracy of RUL and affecting the normal working of rolling element bearing (REB). To solve the above problem, we [...] Read more.
Traditional methods for predicting remaining useful life (RUL) ignore the correlation between physical world data and virtual world data, leading to the low prediction accuracy of RUL and affecting the normal working of rolling element bearing (REB). To solve the above problem, we propose a hybrid method based on digital twin (DT) and long short-term memory (LSTM). The hybrid method combines the high simulation capabilities of DT and the strong data processing capabilities of LSTM. Firstly, we develop a DT system for the life characteristics analysis of an REB. When the DT system is implemented, we can obtain the theoretical value of RUL. Then, the experimental data is used to train the LSTM model. The output of LSTM is the actual value of RUL. Finally, the particle swarm optimization (PSO) algorithm fuses the theoretical values of DT with the actual values of LSTM. The case study demonstrates that the prediction accuracy of the hybrid method is greater than 97.5%, which improves the prediction performance and robustness of RUL. Therefore, the hybrid method is an important technology of REB prediction and health management (PHM). It realizes the early intervention and maintenance of mechanical equipment and ensures the safety of enterprises’ production. Full article
(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)
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22 pages, 4015 KiB  
Article
Dynamics Modeling and Analysis of Rolling Bearings Variable Stiffness System with Local Faults
by Baoliang Guo, Wenlong Wu, Jianxiao Zheng, Yumin He and Jinhua Zhang
Machines 2023, 11(6), 609; https://doi.org/10.3390/machines11060609 - 2 Jun 2023
Cited by 6 | Viewed by 2237
Abstract
By analyzing the support of load-carrying rolling elements when the rolling elements fall into the fault position, the dynamics model of a rolling bearing variable stiffness system with local faults is proposed, considering the retention factor of the contact deformation. Then, this paper [...] Read more.
By analyzing the support of load-carrying rolling elements when the rolling elements fall into the fault position, the dynamics model of a rolling bearing variable stiffness system with local faults is proposed, considering the retention factor of the contact deformation. Then, this paper researches the change of effective contact stiffness, contact deformation, contact force, and the total effective stiffness of the rolling elements. The results show that the contact stiffness of the rolling elements abruptly decreases when the rolling elements fall into the fault position. The contact deformation and contact force of the load-carrying rolling elements in the load zone increase, rebalancing the external radial load while causing a sudden reduction in the total effective stiffness, resulting in the vibration of the system. When different rolling elements fall into the outer ring fault position, the change in total effective stiffness and the system response are equal in magnitude. Additionally, there is a significant outer race fault characteristic frequency accompanied by frequency multiplication in the fault characteristic spectrums. When different rolling elements fall into the inner race fault position, the total effective stiffness is modulated by the inner race rotation and varies dramatically, resulting in the amplitude of the system time domain vibration response also being modulated by the inner race rotation and varying dramatically. Additionally, there is a significant inner race rotational frequency accompanied by frequency multiplication, an inner race fault characteristic frequency accompanied by frequency multiplication, and a side frequency in the fault characteristic spectrums. The research can provide some reference for the effective diagnosis of the rolling bearing fault. Full article
(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)
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Review

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28 pages, 16602 KiB  
Review
Current Status of Research on Hybrid Ceramic Ball Bearings
by Bing Su, Chunhao Lu and Chenghui Li
Machines 2024, 12(8), 510; https://doi.org/10.3390/machines12080510 - 29 Jul 2024
Cited by 2 | Viewed by 2363
Abstract
Rolling element bearings are essential components in modern mechanical equipment, providing crucial support for rotating parts. Hybrid ceramic ball bearings, consisting of steel rings and ceramic balls, have gained popularity in high-speed machinery to enhance performance. These bearings offer advantages such as longer [...] Read more.
Rolling element bearings are essential components in modern mechanical equipment, providing crucial support for rotating parts. Hybrid ceramic ball bearings, consisting of steel rings and ceramic balls, have gained popularity in high-speed machinery to enhance performance. These bearings offer advantages such as longer fatigue life, improved performance, and higher speeds. Extensive research by scholars has been conducted to promote the wider adoption of hybrid ceramic ball bearings. This paper compiles relevant studies on hybrid ceramic bearings, organizing literature related to their lifetime, arranging literature pertaining to their performance analysis from the perspective of analytical methods, and collating literature on their lubrication techniques from the angle of lubrication methods. This paper covers research on lifetime modeling, fatigue spalling, wear, mechanical and tribological properties, dynamic performance, thermal analysis, temperature considerations, and lubrication techniques of hybrid ceramic ball bearings. The aim is to provide readers and researchers with a comprehensive overview of these innovative bearings. Full article
(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)
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36 pages, 2081 KiB  
Review
From Innovation to Standardization—A Century of Rolling Bearing Life Formula
by Tatjana Lazović, Aleksandar Marinković, Ivana Atanasovska, Miloš Sedak and Blaža Stojanović
Machines 2024, 12(7), 444; https://doi.org/10.3390/machines12070444 - 27 Jun 2024
Cited by 3 | Viewed by 2801
Abstract
This review paper is an homage to Arvid Palmgren’s pioneering paper on rolling bearing service life to highlight its relevance a century later. It follows the evolution of bearing service life theory from Palmgren’s fundamental research to the contemporary international standard ISO 281. [...] Read more.
This review paper is an homage to Arvid Palmgren’s pioneering paper on rolling bearing service life to highlight its relevance a century later. It follows the evolution of bearing service life theory from Palmgren’s fundamental research to the contemporary international standard ISO 281. Palmgren’s theory, based on the previously published papers of Stribeck and Hertz, laid the basis for the later development of bearing service life assessment methodology. Based on the Weibull theory of probability of damage, Lundberg and Palmgren introduced stochastic elements to explain the effect of reliability on bearing service life prediction. Harris and Ioannides, who made a significant contribution to the revision of the international standard on rolling bearing load rating and rating life are mentioned as well. Zaretsky’s critical analysis also was not neglected in this review, due to a different approach respecting the original influence of material properties and bearing performances. Despite standardization, ongoing research by leading advanced bearing industries and academic institutions continues to refine methodologies for service life assessment. Through a comprehensive review and analysis, this paper offers insight into the current state of bearing service life theory, highlighting the collaborative efforts bringing progress in this field. Full article
(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)
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