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Lubricants
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New Horizons in Machine Learning Applications for Tribology
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New Horizons in Machine Learning Applications for Tribology

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3775

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 in engineering science, and its understanding provides us with solutions for future technical challenges in health, energy solutions, mobility, and sustainability. 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 existing knowledge. 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 or even real-time way.

The first two editions of the Special Issue “Machine Learning in Tribology” and “Recent Advances in Machine Learning in Tribology” already demonstrated the variety of potential areas of applications and that the potential goes beyond purely academic aspects into actual industrial applications.

The reception of the first two editions by the readers exceeded our expectations and, together with the Editorial Office of Lubricants, we are now proud to launch the third edition of the Special Issue entitled “New Horizons in Machine Learning Applications for Tribology”, aiming to cover 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.

Prof. 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
  • condition monitoring
  • composite materials
  • surface modifications
  • lubricants and additives

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Related Special Issues

Published Papers (4 papers)

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Research

24 pages, 35679 KiB  
Article
Rolling Bearing Dynamics Simulation Information-Assisted Fault Diagnosis with Multi-Adversarial Domain Transfer Learning
by Zhe Li, Zhidan Zhong, Zhihui Zhang, Wentao Mao and Weiqi Zhang
Lubricants 2025, 13(3), 116; https://doi.org/10.3390/lubricants13030116 - 7 Mar 2025
Viewed by 380
Abstract
To address the issues of negative transfer and reduced stability in transfer learning models for rolling bearing fault diagnosis under variable working conditions, an unsupervised multi-adversarial transfer learning fault diagnosis algorithm based on bearing dynamics simulation data is proposed. Firstly, the algorithm constructs [...] Read more.
To address the issues of negative transfer and reduced stability in transfer learning models for rolling bearing fault diagnosis under variable working conditions, an unsupervised multi-adversarial transfer learning fault diagnosis algorithm based on bearing dynamics simulation data is proposed. Firstly, the algorithm constructs both a global domain classifier and a subdomain classifier. In the subdomain classifier, the simulated vibration signal, which contains rich bearing fault label information, is generated by constructing dynamic equations to replace the label prediction of target domain data, thereby achieving alignment of marginal and conditional distributions. Simultaneously, an improved loss function with embedded maximum mean discrepancy is designed to reduce the feature distribution gap between source and target domain data. Finally, a weight allocation mechanism for source domain and simulation domain samples is developed to promote positive transfer and suppress negative transfer. Experiments were conducted using the Paderborn University dataset and the Huazhong University of Science and Technology dataset, achieving accuracy rates of 89.457% and 96.436%, respectively. The results show that, in comparison with existing unsupervised cross-domain fault diagnosis methods, the proposed method demonstrates significant improvements in diagnostic accuracy and stability, demonstrating its superiority in rolling bearing fault diagnosis under variable operational conditions. Full article
(This article belongs to the Special Issue New Horizons in Machine Learning Applications for Tribology)
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21 pages, 19793 KiB  
Article
RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network
by Jiaping Shen, Haiting Zhou, Muda Jin, Zhongping Jin, Qiang Wang, Yanchun Mu and Zhiming Hong
Lubricants 2025, 13(2), 81; https://doi.org/10.3390/lubricants13020081 - 12 Feb 2025
Viewed by 586
Abstract
Due to the complex changes in the physical and chemical properties of rolling bearings from degradation to failure, most model-driven and data-driven methods generally suffer from insufficient accuracy and robustness in predicting the remaining useful life of rolling bearings. To address this challenge, [...] Read more.
Due to the complex changes in the physical and chemical properties of rolling bearings from degradation to failure, most model-driven and data-driven methods generally suffer from insufficient accuracy and robustness in predicting the remaining useful life of rolling bearings. To address this challenge, this paper proposes a data-driven artificial neural network method, namely the CNN-LSTM bearing remaining life prediction model based on the fruit fly optimization algorithm (FOA). This method utilizes the deep feature mining capabilities of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to effectively extract spatial features and temporal information sequences from the dataset. In addition, introducing FOA enables the model to dynamically adjust the hidden layers and thresholds while optimizing the optimal path, thereby finding the best solution. This article conducts ablation experiments on the model using the acceleration life dataset of IEEE PHM 2012 rolling bearings. The experimental results show that the FOA-CNN-LSTM model proposed in this paper significantly outperforms other comparative methods in RUL prediction accuracy and stability, verifying its effectiveness and innovation in dealing with complex degradation processes. This method helps to take preventive measures before faults occur, thereby reducing economic losses and having important practical significance for predicting the remaining life of rolling bearings. Full article
(This article belongs to the Special Issue New Horizons in Machine Learning Applications for Tribology)
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18 pages, 3858 KiB  
Article
Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments
by Yuxiao Zhao, Leyu Lin and Alois K. Schlarb
Lubricants 2024, 12(12), 423; https://doi.org/10.3390/lubricants12120423 - 30 Nov 2024
Viewed by 874
Abstract
This study outlines the development and optimization of a Long Short-Term Memory (LSTM) network designed to analyze and classify time-series data from tribological experiments, with a particular emphasis on identifying stationary phases. The process of fine-tuning key hyperparameters was systematically optimized through Bayesian [...] Read more.
This study outlines the development and optimization of a Long Short-Term Memory (LSTM) network designed to analyze and classify time-series data from tribological experiments, with a particular emphasis on identifying stationary phases. The process of fine-tuning key hyperparameters was systematically optimized through Bayesian optimization, coupled with K-fold cross-validation to minimize the inherent randomness associated with training neural networks. The refined LSTM network achieved a weighted average accuracy of 84%, demonstrating a high level of agreement between the network’s identified stationary phases and those manually determined by researchers. This result suggests that LSTM networks can reliably mimic manual identification processes in tribological data, providing a promising avenue for automating data analysis. The study underscores the potential of neural networks to transcend their traditional role in predictive modeling within tribology, opening up new possibilities for their application across a broader spectrum of tasks within the field. Full article
(This article belongs to the Special Issue New Horizons in Machine Learning Applications for Tribology)
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27 pages, 5605 KiB  
Article
Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm
by Weiqing Sun, Yue Wang, Xingyi You, Di Zhang, Jingyi Zhang and Xiaohu Zhao
Lubricants 2024, 12(7), 239; https://doi.org/10.3390/lubricants12070239 - 2 Jul 2024
Cited by 4 | Viewed by 1293
Abstract
(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods [...] Read more.
(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems. Full article
(This article belongs to the Special Issue New Horizons in Machine Learning Applications for Tribology)
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