Advances in Artificial Intelligence for Vibration-Based Life-Time Monitoring

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2355

Special Issue Editors

Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands
Interests: artificial intelligence; uncertainty quantification; vibration; NDT; SHM
Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands
Interests: artificial intelligence; manufacturing process
Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands
Interests: SHM; PHM; prognosis; diagnosis; artificial intelligence

Special Issue Information

Dear Colleagues,

Over the years, vibration-based methods have been utilized extensively for monitoring and assessing the health of systems and structures in various applications and thus, evolved from traditional methods to artificial intelligence (AI)-based methods. Although AI-based methods have become very popular among researchers and engineers, their performances depend on the quality and quantity of the available training data. This motivates the development of new AI methods to deal with upcoming challenges.

This Special Issue aims to provide a platform for researchers and engineers to share their latest theoretical achievements and/or engineering experiences. This could facilitate the identification of critical issues and challenges for future studies in the field. The theoretical papers accepted into this Special Issue are expected to contain original ideas and potential solutions for resolving real problems.

Topics include, but are not limited to, the development and application of AI in vibration-based methods, such as:

  • Damage detection and localization;
  • Non-destructive testing;
  • Condition monitoring;
  • Structural health monitoring;
  • Damage diagnosis and prognosis;
  • Predictive maintenance;
  • Decision-making.

Dr. Vahid Yaghoubi
Dr. Nathan Eskue
Dr. Nick Eleftheroglou
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. 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

  • artificial intelligence
  • vibration
  • non-destructive testing
  • structural health monitoring
  • predictive maintenance
  • life cycle assessment

Published Papers (1 paper)

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Research

21 pages, 5774 KiB  
Article
Evaluation of Rolling Bearing Performance Degradation Based on Comprehensive Index Reduction and SVDD
by Hongwei Xin, Haidong Zhang, Yanjun Yang and Jianguo Wang
Machines 2022, 10(8), 677; https://doi.org/10.3390/machines10080677 - 10 Aug 2022
Cited by 3 | Viewed by 1834
Abstract
The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in [...] Read more.
The evaluation of rolling bearing performance degradation has important implications for the prediction and health management (PHM) of rotating equipment. A method for evaluation of rolling bearing performance degradation based on comprehensive index reduction and support vector data description (SVDD) is proposed in this study. Firstly, the improved variational mode decomposition (VMD) method was used to decompose vibration signals, and the defect frequency amplitude ratio index which is sensitive to early faults is extracted. Secondly, a comprehensive feature index set of rolling bearings is constructed by combining traditional time-domain and time–frequency-domain indexes, and the main features are extracted by the dimensionality reduction algorithm of locally linear embedding (LLE). Finally, the SVDD evaluation model was utilized to characterize and evaluate the rolling bearing lifetime degradation process using the distance from the test sample to the trained hypersphere center. Results showed that the proposed comprehensive degradation index can accurately detect the occurrence of early weak fault stage of rolling bearings and objectively reveal the performance degradation process of rolling bearings. Full article
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