Wear Mechanism Identification and State Prediction of Tribo-Parts
A special issue of Lubricants (ISSN 2075-4442).
Deadline for manuscript submissions: 20 May 2024 | Viewed by 2070
Special Issue Editors
Interests: wear debris analysis; wear mechanism identification; machine condition monitoring
Special Issue Information
Dear Colleagues,
Wear is the inevitable failure of tribo-parts in a machine, and the wear failure may exhibit different forms according to its physical mechanism. Therefore, wear process monitoring, involving wear mechanism identification and state prediction, play an important role in determining the ongoing wear failures in a running machine.
This Special Issue calls for a collection of both research and review papers providing contributions toward a better understanding of the wear behavior of tribo-parts, developing novel wear mechanism identification methods, and improving wear state prediction methodology and models. Both experimental and numerical-related research is highly encouraged. The Special Issue seeks to provide an opportunity for authors to gather and share insights and achievements in the field of assessment of the wear process of tribo-parts.
Dr. Shuo Wang
Dr. Ying Du
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
- wear monitoring
- wear mechanism identification
- wear state prediction
- tribological performance
- friction and wear
- engineering application
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title (tentative): Wear influence on optimal contouring of conical plain bearings as wind turbine main bearing
Authors: Thomas Decker, Jan Euler, Timm Jakobs, Georg Jacobs, Julian Röder
Affiliation: Center for Wind Power Drives, RWTH Aachen University, 52074 Aachen, Germany
Abstract: Electrical Energy harvested by wind turbines already constitutes the largest portion of the current electricity mix in Germany and a rising proportion worldwide. With their increasing importance for the energy sector, wind turbine availability and reliability are of ever more significance during turbine design. One component which is quite prone to unplanned failure is the wind turbine’s main bearing. A damaged main bearing results in long downtimes and costly repairs as the drivetrain needs to be dismantled using large cranes ore specialised crane vessels for offshore turbines. One possible remedy is the use of plain bearings as main bearings instead of the commonly used rolling element bearings. Plain bearing main bearings can be designed in segments and thus potentially repaired up-tower without the costly dismantling of the drivetrain and thus drastically reduce repair costs in case of bearing failure. To this end the novel FlexPad bearing design was developed at the CWD.
As with all plain bearings, wear is a crucial aspect that needs to be accounted for during the design stage. In this work an experimentally validated wear model for the FlexPad bearing is presented. The influence of wear during idling and start-stop is investigated in its effect on the hydrodynamic performance of the bearing under production conditions. Focus is on the influence of wear on the sliding segments contours and the potential optimization options to minimize wear and increase performance via ideal contouring.
Title (tentative): A novel method for aero-engine rolling bearing fault diagnosis based on oil condition monitoring
Authors: Ying Du, Yue Zhang, Yanchao Zhang
Affiliation: School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi’an, Shaanxi 710048, China
Abstract: Rolling bearings are widely used in the field of aerospace, which is related to the safety of rotating machinery and even the whole aero-engine. Therefore, fault diagnosis and residual useful life prediction of rolling bearings are crucial. With the development of fault diagnosis under big data, there are still shortcomings, such as insufficient data histories in practice. In this paper, a bearing fault diagnosis model based on generative network (GAN) and convolutional neural network (CNN) is established. Firstly, oil condition monitoring is used to get the observed data, which can indicate the status of the rolling bearing. Secondly, generative network is used to generate the real data histories. Thirdly, the expanded data set is divided into the training set and the testing set. Finally, the proposed model can be verified with the data sets, and comparisons are conducted to show the effectiveness and accuracy of our model.
