Tribology and Machine Learning: New Perspectives and Challenges

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 8255

Special Issue Editors


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Guest Editor
Department of Industrial Engineering, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
Interests: mechatronic systems; frictional modeling and model-based control in automotive transmissions; lubrication in internal combustion engines and journal bearings; effects of nanoparticles as friction reducer additives; vibration measurement methods
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Guest Editor
Dipartimento di Ingegneria Civile e Industriale, University of Pisa, Largo Lazzarino, 56122 Pisa, Italy
Interests: mixed friction; fluid friction; seizure/scoring/scuffing; elastohydrodynamic lubrication; mixed lubrication/transition of lubrication regimes; journal bearings; gears/cams; joint prosthesis

Special Issue Information

Dear Colleagues,

Machine learning (ML) and Artificial Intelligence (AI) approaches have found their path into tribology world among other broad areas of scientific disciplines, where such novel techniques can support sorting through the complexity of patterns and identifying trends within multiple interacting features.

There have been recent advancements in the application of machine learning methods to improve the tribological behaviour of materials, machine element operation, shapes, coatings, etc. Indeed, published articles found in the literature cover many fields of tribology from novel materials to surface engineering and nanolubricants. Accordingly, the targets of the proposed numerical algorithms are varied, ranging from artificial neural networks and decision trees to random forest and rule-based learners to support vector machines. Therefore, this Special Issue aims to gather the more recent trends and applications of machine learning approaches in tribology.

Prof. Dr. Adolfo Senatore
Prof. Enrico Ciulli
Prof. Dr. Giuseppe Carbone
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

  • tribology
  • machine learning
  • artificial neural networks
  • analysis
  • prediction
  • optimization

Published Papers (6 papers)

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Research

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17 pages, 11168 KiB  
Article
Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks
by Tomasz Trzepieciński, Krzysztof Szwajka and Marek Szewczyk
Lubricants 2024, 12(2), 50; https://doi.org/10.3390/lubricants12020050 - 09 Feb 2024
Viewed by 938
Abstract
This article presents the results of an analysis of the influence of friction process parameters on the coefficient of friction of steel sheets 1.0347 (DC03), 1.0338 (DC04) and 1.0312 (DC05). A special tribometer was designed and manufactured in order to simulate the friction [...] Read more.
This article presents the results of an analysis of the influence of friction process parameters on the coefficient of friction of steel sheets 1.0347 (DC03), 1.0338 (DC04) and 1.0312 (DC05). A special tribometer was designed and manufactured in order to simulate the friction phenomenon occurring in the blankholder area in deep drawing operations. Lubricant was supplied to the contact zone under pressure. The value of the coefficient of friction was determined under various contact pressures and lubrication conditions. Multi-layer artificial neural networks (ANNs) were used to predict the value of the coefficient of friction. The input parameters considered were the kinematic viscosity of lubricants, contact pressure, lubricant pressure, selected mechanical properties and basic surface roughness parameters of sheet metals. The value of the coefficient of friction of 1.0312 steel sheets was predicted based on the results of friction tests on 1.0347 and 1.0338 steel sheets. Many ANN models were built to find a neural network that will provide the best prediction performance. It was found that to ensure a high performance of ANN prediction, it is necessary to simultaneously take into account all the considered roughness parameters (Sa, Ssk and Sku). The predictive performance of the ‘best’ network was greater than R2 = 0.98. The lubricant pressure had the greatest impact on the coefficient of friction. Increasing the value of this parameter reduces the value of the coefficient of friction. However, the greater the contact pressure, the smaller the beneficial effect of pressure-assisted lubrication. The third parameter of the friction process, the kinematic viscosity of the oil, exhibited the smallest impact on the coefficient of friction. Full article
(This article belongs to the Special Issue Tribology and Machine Learning: New Perspectives and Challenges)
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17 pages, 4652 KiB  
Article
Time-Frequency Fusion Features-Based GSWOA-KELM Model for Gear Fault Diagnosis
by Qin Hu, Haiting Zhou, Chengcheng Wang, Chenxi Zhu, Jiaping Shen and Peng He
Lubricants 2024, 12(1), 10; https://doi.org/10.3390/lubricants12010010 - 29 Dec 2023
Viewed by 1194
Abstract
To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to [...] Read more.
To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively. Full article
(This article belongs to the Special Issue Tribology and Machine Learning: New Perspectives and Challenges)
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24 pages, 10282 KiB  
Article
Research on an Intelligent Classification Algorithm of Ferrography Wear Particles Based on Integrated ResNet50 and SepViT
by Lei He, Haijun Wei and Wenjie Gao
Lubricants 2023, 11(12), 530; https://doi.org/10.3390/lubricants11120530 - 13 Dec 2023
Viewed by 1236
Abstract
The wear particle classification algorithm proposed is based on an integrated ResNet50 and Vision Transformer, aiming to address the problems of a complex background, overlapping and similar characteristics of wear particles, low classification accuracy, and the difficult identification of small target wear particles [...] Read more.
The wear particle classification algorithm proposed is based on an integrated ResNet50 and Vision Transformer, aiming to address the problems of a complex background, overlapping and similar characteristics of wear particles, low classification accuracy, and the difficult identification of small target wear particles in the region. Firstly, an ESRGAN algorithm is used to improve image resolution, and then the Separable Vision Transformer (SepViT) is introduced to replace ViT. The ResNet50-SepViT model (SV-ERnet) is integrated by combining the ResNet50 network with SepViT through weighted soft voting, enabling the intelligent identification of wear particles through transfer learning. Finally, in order to reveal the action mechanism of SepViT, the different abrasive characteristics extracted by the SepViT model are visually explained using the Grad-CAM visualization method. The experimental results show that the proposed integrated SV-ERnet has a high recognition rate and robustness, with an accuracy of 94.1% on the test set. This accuracy is 1.8%, 6.5%, 4.7%, 4.4%, and 6.8% higher than that of ResNet101, VGG16, MobileNetV2, AlexNet, and EfficientV1, respectively; furthermore, it was found that the optimal weighting factors are 0.5 and 0.5. Full article
(This article belongs to the Special Issue Tribology and Machine Learning: New Perspectives and Challenges)
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30 pages, 9488 KiB  
Article
Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment
by Chunlei Zhou, Qingfeng Wang, Yang Xiao, Wang Xiao and Yue Shu
Lubricants 2023, 11(10), 423; https://doi.org/10.3390/lubricants11100423 - 02 Oct 2023
Viewed by 914
Abstract
The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial [...] Read more.
The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial Internet environment, equipment-fault diagnosis is faced with the characteristics of large amounts of data, unbalanced data samples, and inconsistent data file lengths. Moreover, there are few research results on the fault diagnosis of rotor systems composed of shafts, impellers or blades, couplings, and tilting pad bearings. There are still shortcomings in the operational risk evaluation of rotor systems. In order to ensure the reliability and safety of rotor systems, an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty (IACWGAN-GP) model is constructed, a fault diagnosis method based on IACWGAN-GP for tilting pad bearings is proposed, and an intelligent fault diagnosis system platform for equipment in an industrial Internet environment is built. The verification results of engineering case data show that the fault diagnosis model based on IACWGAN-GP can adapt to any length of sequential data files, and the automatic identification accuracy of early faults in tilting pad bearings reaches 98.7%. Full article
(This article belongs to the Special Issue Tribology and Machine Learning: New Perspectives and Challenges)
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Review

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27 pages, 2752 KiB  
Review
Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry
by Md Hafizur Rahman, Sadat Shahriar and Pradeep L. Menezes
Lubricants 2023, 11(7), 289; https://doi.org/10.3390/lubricants11070289 - 10 Jul 2023
Cited by 2 | Viewed by 1982
Abstract
Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML [...] Read more.
Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques. Full article
(This article belongs to the Special Issue Tribology and Machine Learning: New Perspectives and Challenges)
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Other

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12 pages, 6501 KiB  
Technical Note
The Prediction of Wear Depth Based on Machine Learning Algorithms
by Chenrui Zhu, Lei Jin, Weidong Li, Sheng Han and Jincan Yan
Lubricants 2024, 12(2), 34; https://doi.org/10.3390/lubricants12020034 - 26 Jan 2024
Viewed by 1198
Abstract
In this work, ball-on-disk wear experiments were carried out on different wear parameters such as sliding speed, sliding distance, normal load, temperature, and oil film thickness. In total, 81 different sets of wear depth data were obtained. Four different machine learning (ML) algorithms, [...] Read more.
In this work, ball-on-disk wear experiments were carried out on different wear parameters such as sliding speed, sliding distance, normal load, temperature, and oil film thickness. In total, 81 different sets of wear depth data were obtained. Four different machine learning (ML) algorithms, namely Random Forest (RF), K-neighborhood (KNN), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) were applied to predict wear depth. By analyzing the performance of several ML algorithms, it is demonstrated that ball bearing wear depth can be estimated by ML models by inputting different parameter variables. A comparative analysis of the performance of the different models revealed that XGB was more accurate than the other ML models at anticipating wear depth. Further analysis of the attribute of feature importance and correlation heatmap of the Pearson correlation reveals that each input feature has an effect on wear. Full article
(This article belongs to the Special Issue Tribology and Machine Learning: New Perspectives and Challenges)
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