Special Issue "Vibration Analysis for Structural Health 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 832

Special Issue Editor

School of Mechanical Engineering, Coventry University, Coventry CV1 5FB, UK
Interests: fault detection; dynamics; vibration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, condition monitoring (CM), fault diagnosis, and the prognosis of structures and machines have become topics of increasing concern. Early fault detection can help to avoid risks of damage and thus save expensive emergency repair costs. Vibration is considered the most commonly measured parameter in the CM of structures, and it is extensively used in various industrial applications However, interpretation of results from vibration analysis requires investigation from various aspects, and therefore, research in this area faces multiple challenges. Many novel and interesting CM methods have been developed in recent years; however, the challenge will always remain of producing a CM system capable of detecting and identifying the presence of a fault at ever earlier stages of its development. The need to maximise equipment and lifetime reliability requires the integration of reliability condition monitoring (CM) and maintenance precision practices.

Dr. Faris Elasha
Guest Editor

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.


  • vibration analysis
  • condition monitoring
  • prognosis
  • diagnosis

Published Papers (1 paper)

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Gearbox Fault Diagnosis Based on Refined Time-Shift Multiscale Reverse Dispersion Entropy and Optimised Support Vector Machine
Machines 2023, 11(6), 646; https://doi.org/10.3390/machines11060646 - 13 Jun 2023
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The fault diagnosis of a gearbox is crucial to ensure its safe operation. Entropy has become a common tool for measuring the complexity of time series. However, entropy bias may occur when the data are not long enough or the scale becomes larger. [...] Read more.
The fault diagnosis of a gearbox is crucial to ensure its safe operation. Entropy has become a common tool for measuring the complexity of time series. However, entropy bias may occur when the data are not long enough or the scale becomes larger. This paper proposes a gearbox fault diagnosis method based on Refined Time-Shifted Multiscale Reverse Dispersion Entropy (RTSMRDE), t-distributed Stochastic Neighbour Embedding (t-SNE), and the Sparrow Search Algorithm Support Vector Machine (SSA-SVM). First, the proposed RTSMRDE was used to calculate the multiscale fault features. By incorporating the refined time-shift method into Multiscale Reverse Dispersion Entropy (MRDE), errors that arose during the processing of complex time series could be effectively reduced. Second, the t-SNE algorithm was utilized to extract sensitive features from the multiscale, high-dimensional fault features. Finally, the low-dimensional feature matrix was input into SSA-SVM for fault diagnosis. Two gearbox experiments showed that the diagnostic model proposed in this paper had an accuracy rate of 100%, and the proposed model performed better than other methods in terms of diagnostic performance. Full article
(This article belongs to the Special Issue Vibration Analysis for Structural Health Monitoring)
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