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Machines
Special Issues
Vibration-Based Machines Wear Monitoring and Prediction
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Vibration-Based Machines Wear Monitoring and Prediction

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4600

Special Issue Editors

Dr. Zoltan-Iosif Korka

E-Mail Website1 Website2
Guest Editor
Department of Engineering Sciences, Babes-Bolyai University, 320085 Reşiţa, Romania
Interests: damage detection; gear design; gear manufacturing; gear testing; vibration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michael I. Friswell

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Swansea University, Swansea SA1 8EN, UK
Interests: morphing aircraft; structural dynamics; structural health monitoring; rotordynamics; smart structures; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of vibration-based machines for wear monitoring and prediction is crucial in the accurate operation of machines, ensuring their optimum operational properties, safety, and integrity. In this respect, an increase in the utilization of vibration-based methods for the monitoring and prediction of wear in rotating machines has been observed in recent years. This evolution has been positively influenced by the following factors: advances in measurement techniques and the devices employed in vibration engineering, and the development of mathematical tools for signal processing and conditioning. All these factors impact vibration-based wear monitoring and prediction in machines and their components.

We are pleased to invite you to contribute to this Special Issue, which aims to collect interdisciplinary contributions on vibration-based machines for wear monitoring and prediction.

This Special Issue also aims to address the monitoring of wear in machines and the task of damage prediction in relation to numerical simulation and theoretical studies; this is in addition to practical solutions that are applicable to vibration-generating devices and rotating machines, the structural elements of heavy machines, vehicles, and so on.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Methods and aparatus of vibration-based wear monitoring;
  • Advanced signal processing methods for vibration-based wear monitoring;
  • Advanced theoretical discussions on wear propagation in technical systems;
  • Wear monitoring and prediction in rotating machines;
  • Wear prediction in the structural elements of machines;
  • Wear modeling and simulations;
  • Remaining useful life prediction;
  • Practical cases of vibration-based wear monitoring and prediction;
  • Assessment of wear identification.

We look forward to receiving your contributions.

Dr. Zoltan-Iosif Korka
Prof. Dr. Michael I. Friswell
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

  • condition monitoring
  • prediction
  • rotating machine
  • simulation
  • signal processing
  • vibration
  • wear monitoring

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Published Papers (4 papers)

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Research

19 pages, 1358 KiB  
Article
Friction Monitoring in Kaplan Turbines
by Lars-Johan Sandström, Kim Berglund, Pär Marklund and Gregory F. Simmons
Machines 2025, 13(4), 313; https://doi.org/10.3390/machines13040313 - 11 Apr 2025
Viewed by 257
Abstract
Hydropower is important in the modern power system due to its ability to quickly adjust production. More frequent use of this ability may lead to increased maintenance needs, highlighting the importance of research in condition monitoring for hydropower. This study suggests a model [...] Read more.
Hydropower is important in the modern power system due to its ability to quickly adjust production. More frequent use of this ability may lead to increased maintenance needs, highlighting the importance of research in condition monitoring for hydropower. This study suggests a model approach for friction monitoring of the bearings inside the Kaplan turbine’s hub. The approach is developed for when normal and anomalous data exist. The study compares isolation forest (iForest), local outlier factor (LOF), one-class support vector machine (OC-SVM), and Mahalanobis distance (MD) for anomaly detection, where iForest and OC-SVM appear to be good choices due to their robust performance. A moving decision filter (MDF) is fed with the output from the anomaly detection models to classify the data as normal or anomalous. The parameters in the MDF are optimized with Bayesian optimization to increase the performance of the models. The approach is tested using data from two actual hydropower turbines. The study shows that the model approach works for both turbines. However, the parameter optimization must be performed separately for each turbine. Full article
(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)
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22 pages, 5974 KiB  
Article
Estimation of Vibration-Induced Fatigue Damage in a Tracked Vehicle Suspension Arm at Critical Locations Under Real-Time Random Excitations
by Ayaz Mahmood Khan, Muhammad Shahid Khalil and Muhammad Muzammil Azad
Machines 2025, 13(4), 257; https://doi.org/10.3390/machines13040257 - 21 Mar 2025
Viewed by 589
Abstract
Probabilistic random vibration can speed up wear and tear on several components of the tracked vehicle, including the track system, drivetrain, and suspension. Extended exposure to high levels of vibration can cause structural damage to the vehicle frame and other critical components. Assessing [...] Read more.
Probabilistic random vibration can speed up wear and tear on several components of the tracked vehicle, including the track system, drivetrain, and suspension. Extended exposure to high levels of vibration can cause structural damage to the vehicle frame and other critical components. Assessing random vibration in track vehicles requires a comprehensive approach that considers both the root causes and potential consequences of the vibrations. This random vibration significantly influences the structural performance of suspension arm which is key component of tracked vehicle. Damage due to fatigue is conventionally computed using time domain loaded signals with stress or strain data. This approach generally holds good when loading is periodic in nature but not be a good choice when dynamic resonance is in process. In this case an alternative frequency domain fatigue life analysis is used where the random loads and responses are characterized using a concept called Power spectral density (PSD). The current research article investigates the fatigue damage characteristics of a tracked vehicle suspension arm considering the dynamic loads induced by traversing on smooth and rough terrain. The analysis focusses on assessing the damage and stress response Power spectral density (PSD) ground-based excitation which is termed PSD-G acceleration. Quasi Static Finite Element Method based approach is used to simulate the operational conditions experienced by the suspension arm. Through comprehensive numerical simulations, the fatigue damage accumulation patterns are examined, providing insights into the structure integrity and performance durability of the suspension arm under varying operational scenarios. The obtained stress response PSD data and fatigue damage showed that the rough terrain response exhibits higher stresses in suspension arm. The accumulated stresses in case of rough terrain may prompt to brittle failure at specific critical locations. This research contributes to the advancement to the design and optimization strategies for tracked vehicle components enhancing their reliability and longevity in demanding operational environments. Full article
(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)
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21 pages, 10018 KiB  
Article
Vibration-Based Anomaly Detection in Industrial Machines: A Comparison of Autoencoders and Latent Spaces
by Luca Radicioni, Francesco Morgan Bono and Simone Cinquemani
Machines 2025, 13(2), 139; https://doi.org/10.3390/machines13020139 - 12 Feb 2025
Cited by 1 | Viewed by 1276
Abstract
In industrial settings, machinery components inevitably wear and degrade due to friction between moving parts. To address this, various maintenance strategies, including corrective, preventive, and predictive maintenance, are commonly employed. This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This [...] Read more.
In industrial settings, machinery components inevitably wear and degrade due to friction between moving parts. To address this, various maintenance strategies, including corrective, preventive, and predictive maintenance, are commonly employed. This paper focuses on predictive maintenance through vibration analysis, utilizing data-driven models. This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. By transforming vibration signals into images using the Synchrosqueezing Transform (SST), this research leverages the strengths of convolutional neural networks (CNNs) in image processing, which have proven effective in AD, especially at the pixel level. The methodology involves training CAEs and VAEs on data from machinery in healthy condition and testing them on new data samples representing different levels of system degradation. The results indicate that models with spatial latent spaces outperform those with dense latent spaces in terms of reconstruction accuracy and AD capabilities. However, VAEs did not yield satisfactory results, likely because reconstruction-based metrics are not entirely useful for AD purposes in such models. This study also highlights the potential of ReLU residuals in enhancing the visibility of anomalies. The data used in this study are openly available. Full article
(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)
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12 pages, 2863 KiB  
Article
The Effects of Adding TiO2 and CuO Nanoparticles to Fuel on Engine and Hand–Arm Driver Vibrations
by Ali Adelkhani, Peyman Nooripour and Ehsan Daneshkhah
Machines 2024, 12(10), 724; https://doi.org/10.3390/machines12100724 - 13 Oct 2024
Viewed by 1202
Abstract
Occupant comfort is a key consideration in automobile dynamics, with vibrations potentially causing long-term physical discomfort, especially for drivers. This study investigates the impact of adding TiO2 and CuO nanoparticles to fuel on engine-induced vibrations. Experiments were conducted at various nanoparticle concentrations [...] Read more.
Occupant comfort is a key consideration in automobile dynamics, with vibrations potentially causing long-term physical discomfort, especially for drivers. This study investigates the impact of adding TiO2 and CuO nanoparticles to fuel on engine-induced vibrations. Experiments were conducted at various nanoparticle concentrations (0, 50, 100, and 150 ppm) and engine speeds (1000, 2000, and 3000 rpm). Key performance metrics, including kinematic viscosity, density, heating value, thermal conductivity, and brake power (BP), were analyzed. The results indicated that increasing nanoparticle concentration led to a rise in BP. The highest reduction in root mean square (RMS) vibration accelerations occurred at 3000 rpm and 150 ppm, with vibration reductions of 30.33% for CuO and 28.61% for TiO2. Additionally, 8–10% of engine vibrations were transmitted to the steering wheel. The use of 150 ppm CuO nanoparticles resulted in reduced vibration transmission to the steering wheel at all tested speeds. These findings suggest that nanoparticle-enhanced fuels can significantly reduce engine vibrations, potentially improving driver comfort and reducing wear on vehicle components. Full article
(This article belongs to the Special Issue Vibration-Based Machines Wear Monitoring and Prediction)
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