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Applied Sciences
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Technical Diagnostics and Predictive Maintenance, 2nd Edition
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Technical Diagnostics and Predictive Maintenance, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3867

Special Issue Editors

Dr. Tibor Krenicky

E-Mail Website
Guest Editor
Department of Design and Monitoring of Technical Systems, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, Bayerova 1, 080 01 Presov, Slovakia
Interests: nanomaterials; mechanical engineering; virtual instrumentation; technical diagnostics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ján Piteľ

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, 080 01 Presov, Slovakia
Interests: monitoring and control of machines; mechatronic systems
Special Issues, Collections and Topics in MDPI journals
Dr. Kamil Židek

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, 080 01 Presov, Slovakia
Interests: data acquisition; digital twins; identification technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to present the state of the research on the topics of theory, modelling, monitoring, and diagnostics of the operation of technical systems, data processing, and analysis focused on faults detection, along with predictive maintenance theory and methods.

Research subjects should be investigated by using specific models, tools, and instruments, along with their verification and evaluation of the operational states of technical systems. The knowledge presented in this Special Issue, as well as methods, technical systems, and their applications, vindicates its strong potential to attract and impress researchers as well as other professionals, and will contribute to the process of giving answers that are still to be given or questions that are still to be formulated.

Contributions should primarily focus on:

  • Technical diagnostic methods and systems
  • Diagnostics of machines and technical systems operational states
  • Optimization of machinery operation and service using diagnostic methods
  • Use of novel methods and technologies in technical diagnostics and maintenance
  • Online monitoring, digital twin, data acquisition, and signal processing
  • Machine learning and AI-based methods in technical diagnostics and predictive maintenance
  • Diagnostic and maintenance utilization of virtualized systems
  • Advanced inspection methods
  • Diagnostics of drives (electric, pneumatic, etc.)
  • Technical systems operation quality and reliability assessment
  • Technical systems operation modelling and characterization
  • Functional surface properties characterization
  • Structural characterization of materials for defects identification
  • Aspects of implementing technical diagnostics and predictive maintenance
  • Safety and health protection aspects of diagnostics and maintenance

Dr. Tibor Krenicky
Prof. Dr. Ján Piteľ
Dr. Kamil Židek
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • technical diagnostics
  • predictive maintenance
  • machine learning
  • operational states
  • technical system reliability

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

30 pages, 1094 KB  
Article
An Unsupervised Data-Driven Framework for Bearing Failure Prognosis via Health Stage Clustering and Artificial Neural Network-Based Remaining Useful Life Estimation
by Charafeddine Khamoudj, Fatima Benbouzid-Si Tayeb, Karima Benatchba and Mohamed Benbouzid
Appl. Sci. 2026, 16(5), 2472; https://doi.org/10.3390/app16052472 - 4 Mar 2026
Viewed by 374
Abstract
Reliable bearing-failure prognosis in induction machines remains a critical research challenge, as it directly impacts system availability, maintenance efficiency, and overall operational safety. To address this challenge, it is essential to develop an online prognostic system capable of continuously assessing bearing health and [...] Read more.
Reliable bearing-failure prognosis in induction machines remains a critical research challenge, as it directly impacts system availability, maintenance efficiency, and overall operational safety. To address this challenge, it is essential to develop an online prognostic system capable of continuously assessing bearing health and predicting future failures in real time. This paper proposes a novel unsupervised data-driven prognostic framework for induction machine bearings that integrates advanced signal processing techniques for the preprocessing step, data clustering to construct bearing health stage (HS), artificial neural network (ANN) forecasting using a designed health indicator (HI) based on the latest historical observations, and a fine-tuning model to improve the estimation of remaining useful life (RUL) for induction machine bearings using vibration and temperature signals provided by the PRONOSTIA and NASA-IMS experimentation platform. The results show that the proposed approach is an effective way for bearing RUL estimation. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance, 2nd Edition)
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35 pages, 10263 KB  
Article
Enhancement of Primary Profile Surface Quality in FFF Printing Through Vibration Monitoring and Active Control
by Peter Gabštur, Marek Kočiško, Jozef Török and Jakub Kaščak
Appl. Sci. 2025, 15(21), 11346; https://doi.org/10.3390/app152111346 - 22 Oct 2025
Cited by 2 | Viewed by 1826
Abstract
Vibrations of the print head and structural components during 3D printing with FFF technology can significantly impact the quality of printed parts, resulting in defects such as ghosting, ringing, and geometric inaccuracies. These undesired effects are primarily caused by mechanical oscillations of the [...] Read more.
Vibrations of the print head and structural components during 3D printing with FFF technology can significantly impact the quality of printed parts, resulting in defects such as ghosting, ringing, and geometric inaccuracies. These undesired effects are primarily caused by mechanical oscillations of the print head, build platform, and frame, induced by dynamic changes in movement speed and inertial forces within the printing mechanism. This study investigates the effectiveness of vibration compensation using an ADXL345 accelerometer to regulate the motion of the print head and build platform on the Ender 3 V2 Neo printer. The experiment consisted of three test series performed under two distinct conditions, without vibration compensation and with active compensation enabled. All tests were carried out using identical baseline printing parameters. The differences in output were evaluated through visual inspection and dimensional analysis of the printed samples. Efficient vibration monitoring and its active control, aimed at suppressing oscillatory phenomena, can enhance both geometric accuracy and surface uniformity. In FFF 3D printing, especially when utilizing increased layer heights such as 0.3 mm, surface roughness (Ra) values in the range of 18 to 25 µm are typically expected, even when optimal process parameters are applied. This study emphasizes the role of active vibration control strategies in additive manufacturing, particularly in enhancing surface quality and dimensional accuracy. The objective is not only to mitigate the adverse effects of dynamic mechanical vibrations but also to determine the extent to which surface roughness can be systematically reduced under defined conditions, such as layer height, print speed, and movement trajectory. The aim is to improve the final product quality without introducing significant compromises in process efficiency. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance, 2nd Edition)
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21 pages, 3800 KB  
Article
Development of Technical Diagnostics for Lubrication in Gear Modules for Advanced Robotic Applications
by Silvia Maláková, Ľubomír Ilečko, Tibor Krenicky and Marian Dzimko
Appl. Sci. 2025, 15(13), 7431; https://doi.org/10.3390/app15137431 - 2 Jul 2025
Cited by 1 | Viewed by 1079
Abstract
The paper focuses on the experimental investigation of the impact of filtration and tribological parameters on the reliability, service life, and functional characteristics of gear mechanisms used in robotics. The primary objective was to analyze the importance of lubricant cleanliness in robotic transmission [...] Read more.
The paper focuses on the experimental investigation of the impact of filtration and tribological parameters on the reliability, service life, and functional characteristics of gear mechanisms used in robotics. The primary objective was to analyze the importance of lubricant cleanliness in robotic transmission modules and to assess the effectiveness of filtration as a preventive and protective measure. As part of the research, a dedicated test rig was designed and developed. Based on the measurements and analyses performed, a significant correlation was confirmed between lubricant contamination levels and degradation phenomena in transmission modules. The study also highlights a sharp increase in contamination during the initial hours of operation, emphasizing the need for early intervention and continuous monitoring. The findings have strong practical potential and are highly relevant for manufacturers of robotic systems, maintenance service providers, and operators of automated production lines. The results contribute to increased system reliability and extended service life, reduced maintenance and repair costs, and improved environmental aspects of robotic system maintenance. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance, 2nd Edition)
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