Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.6
3.1
Journals
Lubricants
Special Issues
Recent Advances in Machine Learning in Tribology
share announcement

Recent Advances in Machine Learning in Tribology

A special issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: closed (1 January 2024) | Viewed by 31563

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Max Marian

E-Mail Website
Guest Editor
1. Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
2. Institute of Machine Design and Tribology (IMKT), Leibniz University Hannover, Germany
Interests: tribology; elastohydrodynamic lubrication; hydrodynamic lubrication; micro-texturing; biotribology; synovial joint tribology; additive manufacturing; DLC coating; 2D materials; MXenes; solid lubricants; composite materials; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stephan Tremmel

E-Mail Website
Guest Editor
Engineering Design and CAD, University of Bayreuth, Bayreuth, Germany
Interests: engineering design; computer-aided engineering; finite element analysis; machine elements; drive technology; rolling bearings; tribology; PVD/PACVD coatings; elastohydrodynamic lubrication; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tribology has been and continues to be one of the most relevant fields and its understanding provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand our existing knowledge of this field. Thereby, machine learning (ML) and artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient manner or even real-time way. The first edition of the Special Issue, “Machine Learning in Tribology”, already demonstrated the variety of potential applications of these methods, beyond purely academic purposes and encompassing industrial applications.

The warm reception of the first edition from its readers exceeded our expectations and now, together with the Editorial Office of Lubricants, we are proud to launch the second edition of this Special Issue, entitled “Recent Advances in Machine Learning in Tribology”, which covers the latest developments from academic and industrial researchers linked to innovations in the broad field of tribology by employing machine learning and artificial intelligence approaches.

Dr. Max Marian
Prof. Dr. Stephan Tremmel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Lubricants 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 2600 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

  • machine learning
  • artificial intelligence
  • knowledge discovery in databases
  • data mining
  • metamodels
  • artificial neural networks
  • classification
  • regression
  • friction
  • lubrication
  • wear
  • rheology
  • machine elements
  • conditions monitoring
  • composite materials
  • surface modifications
  • lubricants and additives

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issues

Published Papers (13 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review, Other

1 pages, 135 KiB  
Editorial
Recent Advances in Machine Learning in Tribology
by Max Marian and Stephan Tremmel
Lubricants 2024, 12(5), 168; https://doi.org/10.3390/lubricants12050168 - 9 May 2024
Viewed by 1167
Abstract
Tribology, the study of friction, wear, and lubrication, has been a subject of interest for researchers exploring the complexities of materials and surfaces [...] Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)

Research

Jump to: Editorial, Review, Other

19 pages, 9725 KiB  
Article
A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning
by Markus Brase, Jonathan Binder, Mirco Jonkeren and Matthias Wangenheim
Lubricants 2024, 12(1), 20; https://doi.org/10.3390/lubricants12010020 - 9 Jan 2024
Cited by 1 | Viewed by 2135
Abstract
Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as [...] Read more.
Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model prediction, without committing to only a few learning algorithms. A large number of 26 regression learning algorithms are used to build machine learning models through supervised learning to evaluate the suitability of different models in the specific application context. In order to select the best model, mathematical metrics and tribological relationships, like Stribeck curves, are applied and compared with each other. The resulting model is utilised in the subsequent friction optimisation step, in which optimal surface texture parameter combinations with the lowest friction coefficients are predicted over a defined interval of relative velocities. Finally, the friction behaviour is evaluated in the context of the model and optimal value combinations of the surface texture parameters are identified for different lubrication conditions. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

23 pages, 3330 KiB  
Article
Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts
by Joe Issa, Alain El Hajj, Philippe Vergne and Wassim Habchi
Lubricants 2023, 11(12), 497; https://doi.org/10.3390/lubricants11120497 - 22 Nov 2023
Cited by 5 | Viewed by 2243
Abstract
This study extends the use of Machine Learning (ML) approaches for lubricant film thickness predictions to the general case of elliptical elastohydrodynamic (EHD) contacts, by considering wide and narrow contacts over a wide range of ellipticity and operating conditions. Finite element (FEM) simulations [...] Read more.
This study extends the use of Machine Learning (ML) approaches for lubricant film thickness predictions to the general case of elliptical elastohydrodynamic (EHD) contacts, by considering wide and narrow contacts over a wide range of ellipticity and operating conditions. Finite element (FEM) simulations are used to generate substantial training and testing datasets that are used within the proposed ML framework. The complete dataset entails 915 samples; split into an 823-sample training dataset and a 92-sample testing dataset, corresponding to 90% and 10% of the combined dataset samples, respectively. The proposed ML model consists of a pre-processing stage in which conventional EHD dimensionless groups are used to minimize the number of inputs into the model, reducing them to only three. The core of the model is based on Gaussian Process Regression (GPR), a powerful ML regression tool, well-suited for small-sized datasets, producing output central and minimum film thicknesses, also in dimensionless form. The last stage is a post-processing one, in which the output film thicknesses are retrieved in dimensional from. The results reveal the capabilities and potential of the proposed ML framework, producing quasi-instantaneous predictions that are far more accurate than conventional film thickness analytical formulae. In fact, the produced central and minimum film thickness predictions are on average within 0.3% and 1.0% of the FEM results, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

21 pages, 11116 KiB  
Article
An AI-Extended Prediction of Erosion-Corrosion Degradation of API 5L X65 Steel
by Ariel Espinoza-Jara, Igor Wilk, Javiera Aguirre and Magdalena Walczak
Lubricants 2023, 11(10), 431; https://doi.org/10.3390/lubricants11100431 - 5 Oct 2023
Viewed by 1591
Abstract
The application of Artificial Neuronal Networks (ANN) offers better statistical accuracy in erosion-corrosion (E-C) predictions compared to the conventional linear regression based on Multifactorial Analysis (MFA). However, the limitations of ANN to require large training datasets and a high number of inputs pose [...] Read more.
The application of Artificial Neuronal Networks (ANN) offers better statistical accuracy in erosion-corrosion (E-C) predictions compared to the conventional linear regression based on Multifactorial Analysis (MFA). However, the limitations of ANN to require large training datasets and a high number of inputs pose a practical challenge in the field of E-C due to the scarcity of data. To address this challenge, a novel ANN method is proposed, structured to a small training dataset and trained with the aid of synthetic data to produce an E-C neural network (E-C NN), applied for the first time in the study of E-C wear synergy. In the process, transfer learning is applied by pre-training and fine-tuning the model. The initial dataset is created from experimental data produced in a slurry pot setup, exposing API 5L X65 steel to a turbulent copper tailing slurry. To the previously known E-C scenario for selected values of flow velocity, particle concentration, temperature, pH, and the content of the dissolved Cu2+, new experimental data of stand-alone erosion and stand-alone corrosion is added. The prediction of wear loss by E-C NN considers individual parameters and their interactions. The main result is that E-C ANN provides better prediction than MFA as evaluated by a mean squared error (MSE) values of 2.5 and 3.7, respectively. The results are discussed in the context of the cross-effect between the proposed prediction model and the resulting estimation of relative contribution to E-C synergy, which is better predicted by the E-C NN. The E-C NN model is concluded to be a viable alternative to MFA, delivering similar prediction with better sensitivity to E-C synergy at shorter computation times when using the same experimental dataset. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

19 pages, 5326 KiB  
Article
Intelligent Tool Wear Monitoring Method Using a Convolutional Neural Network and an Informer
by Xingang Xie, Min Huang, Weiwei Sun, Yiming Li and Yue Liu
Lubricants 2023, 11(9), 389; https://doi.org/10.3390/lubricants11090389 - 11 Sep 2023
Viewed by 1500
Abstract
Tool wear (TW) is the gradual deterioration and loss of cutting edges due to continuous cutting operations in real production scenarios. This wear can affect the quality of the cut, increase production costs, reduce workpiece accuracy, and lead to sudden tool breakage, affecting [...] Read more.
Tool wear (TW) is the gradual deterioration and loss of cutting edges due to continuous cutting operations in real production scenarios. This wear can affect the quality of the cut, increase production costs, reduce workpiece accuracy, and lead to sudden tool breakage, affecting productivity and safety. Nevertheless, since conventional tool wear monitoring (TWM) approaches often employ complex physical models and empirical rules, their application to complex and non-linear manufacturing processes is challenging. As a result, this study presents a TWM model using a convolutional neural network (CNN), an Informer encoder, and bidirectional long short-term memory (BiLSTM). First, local feature extraction is performed on the input multi-sensor signals using CNN. Then, the Informer encoder deals with long-term time dependencies and captures global time features. Finally, BiLSTM captures the time dependency in the data and outputs the predicted tool wear state through the fully connected layer. The experimental results show that the proposed TWM model achieves a prediction accuracy of 99%. It is able to meet the TWM accuracy requirements of real production needs. Moreover, this method also has good interpretability, which can help to understand the critical tool wear factors. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

24 pages, 10359 KiB  
Article
Rolling Bearing Fault Diagnosis across Operating Conditions Based on Unsupervised Domain Adaptation
by Zhidan Zhong, Hao Liu, Wentao Mao, Xinghui Xie and Yunhao Cui
Lubricants 2023, 11(9), 383; https://doi.org/10.3390/lubricants11090383 - 8 Sep 2023
Cited by 6 | Viewed by 1533
Abstract
In practical industrial scenarios, mechanical equipment frequently operates within dynamic working conditions. To address the challenge posed by the incongruent data distribution between source and target domains amidst varying operational contexts, particularly in the absence of labels within the target domain, this study [...] Read more.
In practical industrial scenarios, mechanical equipment frequently operates within dynamic working conditions. To address the challenge posed by the incongruent data distribution between source and target domains amidst varying operational contexts, particularly in the absence of labels within the target domain, this study presents a solution involving deep feature construction and an unsupervised domain adaptation strategy for rolling bearing fault diagnosis across varying working conditions. The proposed methodology commences by subjecting the original vibration signal of the bearing to a fast Fourier transform (FFT) to extract spectral information. Subsequently, an innovative amalgamation of a one-dimensional convolutional layer and an auto-encoder were introduced to construct a convolutional auto-encoder (CAE) dedicated to acquiring depth features from the spectrum. In a subsequent step, leveraging the depth features gleaned from the convolutional auto-encoder, a balanced distribution adaptation (BDA) mechanism was introduced to facilitate the domain adaptation of features from both the source and target domains. The culminating stage entails the classification of adapted features using the K-nearest neighbor (KNN) algorithm to attain cross-domain diagnosis. Empirical evaluations are conducted on two extensively used datasets. The findings substantiate that the proposed approach is capable of accomplishing the cross-domain fault diagnosis task even without labeled data within the target domain. Furthermore, the diagnostic accuracy and stability of the proposed method surpass those of various other migration and deep learning approaches. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

21 pages, 6847 KiB  
Article
Individual Feature Selection of Rolling Bearing Impedance Signals for Early Failure Detection
by Florian Michael Becker-Dombrowsky, Quentin Sean Koplin and Eckhard Kirchner
Lubricants 2023, 11(7), 304; https://doi.org/10.3390/lubricants11070304 - 20 Jul 2023
Cited by 6 | Viewed by 1766
Abstract
Condition monitoring of technical systems has increasing importance for the reduction of downtimes based on unplanned breakdowns. Rolling bearings are a central component of machines because they often support energy-transmitting elements like shafts and spur gears. Bearing damages lead to a high number [...] Read more.
Condition monitoring of technical systems has increasing importance for the reduction of downtimes based on unplanned breakdowns. Rolling bearings are a central component of machines because they often support energy-transmitting elements like shafts and spur gears. Bearing damages lead to a high number of machine breakdowns; thus, observing these has the potential to reduce unplanned downtimes. The observation of bearings is challenging since their behavior in operation cannot be investigated directly. A common solution for this task is the measurement of vibration or component temperature, which is able to show an already occurred bearing damage. Measuring the electrical bearing impedance in situ has the ability to gather information about bearing revolution speed and bearing loads. Additionally, measuring the impedance allows for the detection and localization of damages in the bearing, as early research has shown. In this paper, the impedance signal of five fatigue tests is investigated using individual feature selection. Additionally, the feature behavior is analyzed and explained. It is shown that the three different bearing operational time phases can be distinguished via the analysis of impedance signal features. Furthermore, some of the features show a significant change in behavior prior to the occurrence of initial damages before the vibration signals of the test rig vary from a normal state. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

13 pages, 2571 KiB  
Article
Classification of Lubricating Oil Types Using Mid-Infrared Spectroscopy Combined with Linear Discriminant Analysis–Support Vector Machine Algorithm
by Jigang Xu, Shujun Liu, Ming Gao and Yonggang Zuo
Lubricants 2023, 11(6), 268; https://doi.org/10.3390/lubricants11060268 - 20 Jun 2023
Cited by 4 | Viewed by 1885
Abstract
To realize the classification of lubricating oil types using mid-infrared (MIR) spectroscopy, linear discriminant analysis (LDA) was used for the dimensionality reduction of spectrum data, and the classification model was established based on the support vector machine (SVM). The spectra of the samples [...] Read more.
To realize the classification of lubricating oil types using mid-infrared (MIR) spectroscopy, linear discriminant analysis (LDA) was used for the dimensionality reduction of spectrum data, and the classification model was established based on the support vector machine (SVM). The spectra of the samples were pre-processed by interval selection, Savitzky–Golay smoothing, multiple scattering correction, and normalization. The Kennard–Stone algorithm (K/S) was used to construct the calibration and validation sets. The percentage of correct classification (%CC) was used to evaluate the model. This study compared the results obtained with several chemometric methods: PLS-DA, LDA, principal component analysis (PCA)-SVM, and LDA-SVM in MIR spectroscopy applications. In both calibration and verification sets, the LDA-SVM model achieved 100% favorable results. The PLS-DA analysis performed poorly. The cyclic resistance ratio (CRR) of the calibration set was classified via the LDA and PCA-SVM analysis as 100%, but the CRR of the verification set was not as good. The LDA-SVM model was superior to the other three models; it exhibited good robustness and strong generalization ability, providing a new method for the classification of lubricating oil types by MIR spectroscopy. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Graphical abstract

15 pages, 2917 KiB  
Article
Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment
by Ali Usman, Saad Arif, Ahmed Hassan Raja, Reijo Kouhia, Andreas Almqvist and Marcus Liwicki
Lubricants 2023, 11(6), 254; https://doi.org/10.3390/lubricants11060254 - 9 Jun 2023
Cited by 1 | Viewed by 2232
Abstract
Improving the frictional response of a functional surface interface has been a significant research concern. During the last couple of decades, lubricant oils have been enriched with several additives to obtain formulations that can meet the requirements of different lubricating regimes from boundary [...] Read more.
Improving the frictional response of a functional surface interface has been a significant research concern. During the last couple of decades, lubricant oils have been enriched with several additives to obtain formulations that can meet the requirements of different lubricating regimes from boundary to full-film hydrodynamic lubrication. The possibility to improve the tribological performance of lubricating oils using various types of nanoparticles has been investigated. In this study, we proposed a data-driven approach that utilizes machine learning (ML) techniques to optimize the composition of a hybrid oil by adding ceramic and carbon-based nanoparticles in varying concentrations to the base oil. Supervised-learning-based regression methods including support vector machines, random forest trees, and artificial neural network (ANN) models are developed to capture the inherent non-linear behavior of the nano lubricants. The ANN hyperparameters were fine-tuned with Bayesian optimization. The regression performance is evaluated with multiple assessment metrics such as the root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The ANN showed the best prediction performance among all ML models, with 2.22 × 10−3 RMSE, 4.92 × 10−6 MSE, 2.1 × 10−3 MAE, and 0.99 R2. The computational models’ performance curves for the different nanoparticles and how the composition affects the interface were investigated. The results show that the composition of the optimized hybrid oil was highly dependent on the lubrication regime and that the coefficient of friction was significantly reduced when optimal concentrations of ceramic and carbon-based nanoparticles are added to the base oil. The proposed research work has potential applications in designing hybrid nano lubricants to achieve optimized tribological performance in changing lubrication regimes. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

18 pages, 4068 KiB  
Article
Performance Prediction Model for Hydrodynamically Lubricated Tilting Pad Thrust Bearings Operating under Incomplete Oil Film with the Combination of Numerical and Machine-Learning Techniques
by Konstantinos P. Katsaros and Pantelis G. Nikolakopoulos
Lubricants 2023, 11(3), 113; https://doi.org/10.3390/lubricants11030113 - 4 Mar 2023
Cited by 5 | Viewed by 2129
Abstract
Pivoted pad thrust bearings are common machine elements used in rotating mechanisms in order to support axial loads. The hydrodynamic lubrication of such bearings has been a major subject of many investigations over the years. However, the majority of these investigations are based [...] Read more.
Pivoted pad thrust bearings are common machine elements used in rotating mechanisms in order to support axial loads. The hydrodynamic lubrication of such bearings has been a major subject of many investigations over the years. However, the majority of these investigations are based on full film lubrication models, when, in fact, incomplete oil film profiles appear during various operating conditions, such as startups and shutdowns. The lack of lubricant during operations can have severe impact on the bearing’s performance, affecting its ability to carry the applied axial load. The scope of the current investigation is to combine numerical analysis and machine-learning techniques in order to create a model that predicts the thrust bearing’s performance in terms of the pad’s load-carrying capacity. For this purpose, the 2-D Reynolds equation is solved numerically for a variety of angular velocities and three different lubricants: SAE 20, SAE 30 and SAE 10W40. The position of the lack of lubricant within the oil film’s control volume is studied and evaluated, together with the percentage of oil film coverage in the inlet of the pad. The results of the numerical analysis are used as input, in order to train and evaluate three different machine-learning models: Quadratic Polynomial Regression, Quadratic SVM Regression and Regression Trees. The results showed that the position of the film incompleteness affects the ability of the bearing to carry the axial load. At the same time as less lubricant entered the domain, the pressure drop could reach lower values, up to 93%. From the studied lubricants, SAE 10W40 was the one that showed the best performance results during incomplete oil film operation. Finally, the Quadratic Polynomial Regression model showed the best fit and 99% accuracy in predicting the pad’s load-carrying capacity. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

Review

Jump to: Editorial, Research, Other

19 pages, 8086 KiB  
Review
Physics-Informed Machine Learning—An Emerging Trend in Tribology
by Max Marian and Stephan Tremmel
Lubricants 2023, 11(11), 463; https://doi.org/10.3390/lubricants11110463 - 30 Oct 2023
Cited by 7 | Viewed by 4431
Abstract
Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, [...] Read more.
Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, and lubrication. Traditional machine learning approaches often rely solely on data-driven techniques, lacking the incorporation of fundamental physics. However, PIML approaches, for example, Physics-Informed Neural Networks (PINNs), leverage the known physical laws and equations to guide the learning process, leading to more accurate, interpretable and transferable models. PIML can be applied to various tribological tasks, such as the prediction of lubrication conditions in hydrodynamic contacts or the prediction of wear or damages in tribo-technical systems. This review primarily aims to introduce and highlight some of the recent advances of employing PIML in tribological research, thus providing a foundation and inspiration for researchers and R&D engineers in the search of artificial intelligence (AI) and machine learning (ML) approaches and strategies for their respective problems and challenges. Furthermore, we consider this review to be of interest for data scientists and AI/ML experts seeking potential areas of applications for their novel and cutting-edge approaches and methods. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Graphical abstract

21 pages, 2349 KiB  
Review
Improvement of Generative Adversarial Network and Its Application in Bearing Fault Diagnosis: A Review
by Diwang Ruan, Xuran Chen, Clemens Gühmann and Jianping Yan
Lubricants 2023, 11(2), 74; https://doi.org/10.3390/lubricants11020074 - 10 Feb 2023
Cited by 23 | Viewed by 3746
Abstract
A small sample size and unbalanced sample distribution are two main problems when data-driven methods are applied for fault diagnosis in practical engineering. Technically, sample generation and data augmentation have proven to be effective methods to solve this problem. The generative adversarial network [...] Read more.
A small sample size and unbalanced sample distribution are two main problems when data-driven methods are applied for fault diagnosis in practical engineering. Technically, sample generation and data augmentation have proven to be effective methods to solve this problem. The generative adversarial network (GAN) has been widely used in recent years as a representative generative model. Besides the general GAN, many variants have recently been reported to address its inherent problems such as mode collapse and slow convergence. In addition, many new techniques are being proposed to increase the sample generation quality. Therefore, a systematic review of GAN, especially its application in fault diagnosis, is necessary. In this paper, the theory and structure of GAN and variants such as ACGAN, VAEGAN, DCGAN, WGAN, et al. are presented first. Then, the literature on GANs is mainly categorized and analyzed from two aspects: improvements in GAN’s structure and loss function. Specifically, the improvements in the structure are classified into three types: information-based, input-based, and layer-based. Regarding the modification of the loss function, it is sorted into two aspects: metric-based and regularization-based. Afterwards, the evaluation metrics of the generated samples are summarized and compared. Finally, the typical applications of GAN in the bearing fault diagnosis field are listed, and the challenges for further research are also discussed. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Figure 1

Other

11 pages, 3604 KiB  
Technical Note
Ensemble Deep Learning for Wear Particle Image Analysis
by Ronit Shah, Naveen Venkatesh Sridharan, Tapan K. Mahanta, Amarnath Muniyappa, Sugumaran Vaithiyanathan, Sangharatna M. Ramteke and Max Marian
Lubricants 2023, 11(11), 461; https://doi.org/10.3390/lubricants11110461 - 29 Oct 2023
Cited by 2 | Viewed by 1953
Abstract
This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles [...] Read more.
This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles obtained through the extraction and filtration of lubricating oil from a 4-stroke petrol internal combustion engine following varied travel distances. Specifically, this work postulates that the amalgamation of ensemble deep learning, involving the combination of multiple deep learning models, leads to greater accuracy compared to individually trained techniques. To substantiate this hypothesis, a fusion of deep learning methods is implemented, featuring deep convolutional neural network (CNN) architectures including Xception, Inception V3, and MobileNet V2. Through individualized training of each model, accuracies reached 85.93% for MobileNet V2 and 93.75% for Inception V3 and Xception. The major finding of this study is the hybrid ensemble deep learning model, which displayed a superior accuracy of 98.75%. This outcome not only surpasses the performance of the singularly trained models, but also substantiates the viability of the proposed hypothesis. This technical note highlights the effectiveness of utilizing ensemble deep learning methods for extracting wear particle features from SEM images. The demonstrated achievements of the hybrid model strongly support its adoption to improve predictive analytics and gain insights into intricate wear mechanisms across various engineering applications. Full article
(This article belongs to the Special Issue Recent Advances in Machine Learning in Tribology)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-13
Back to TopTop