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Applied Sciences
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Maintenance and Reliability Engineering: Latest Advances and Prospects
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Maintenance and Reliability Engineering: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 30 July 2026 | Viewed by 1172

Special Issue Editors

Dr. Przemyslaw Drozyner

E-Mail Website
Guest Editor
Department of Construction, Operation of Vehicles and Machines, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
Interests: maintenance of machines and equipment; technical diagnosis; quality management
Prof. Dr. Malgorzata Jasiulewicz-Kaczmarek

E-Mail Website
Guest Editor
Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland
Interests: sustainable manufacturing; sustainable maintenance; Sustainability 4.0; sustainability assessment; multi-criteria decision-making methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, in the face of the fourth industrial revolution, topics related to maintenance engineering and system reliability are gaining particular importance. The ongoing digitalization, process automation and implementation of artificial intelligence in production pose new challenges for engineers, but also unique opportunities. Technologies such as the Internet of Things (IoT), analysis of large data sets (Big Data) or machine learning are revolutionizing the approach to monitoring and predicting failures, which allows for the optimization of maintenance processes and increased system reliability. These changes enable not only faster detection of faults, but also the implementation of preventive strategies that reduce the risk of costly downtime. However, the integration of these advanced technologies is also associated with numerous challenges: the need to adapt new tools, train staff and ensure an appropriate level of data security. In this context, effective maintenance management and ensuring reliability are becoming crucial not only for maintaining production continuity, but also for increasing market competitiveness. This Special Issue will present the latest achievements and perspectives in the field of maintenance and reliability engineering, which are gaining importance in the face of dynamic technological changes.

This Special Issue will present a collection of original research papers that deal with newly emerged maintenance and reliability problems of modern technical, antropo-technical and AI-technical systems in order to report innovative ideas and the most recent interesting applications. In collaboration with Applied Sciences (MDPI), up to 20 high-quality papers will be published in this carefully prepared Special Issue after a strict peer-review process. Submissions dealing with the following topics (but not limited to these) are very much welcomed:

  1. The use of artificial intelligence and machine learning in failure prediction;
  2. The Internet of Things (IoT) in monitoring and managing the condition of machines;
  3. Lifecycle management of machines and devices in the era of Industry 4.0;
  4. Data and system security in the context of maintenance in Industry 4.0;
  5. Optimization of maintenance processes using advanced data analysis (Big Data); challenges and perspectives for training and technology adaptation in organizations;
  6. Implementation of the ISO 55000 standard in asset management and maintenance;
  7. The role of reliability in ensuring production continuity in the era of Industry 4.0.

Dr. Przemyslaw Drozyner
Dr. Malgorzata Jasiulewicz-Kaczmarek
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

  • maintenance engineering
  • reliability engineering
  • industrial reliability
  • Industry 4.0
  • smart maintenance
  • machine learning in maintenance
  • data analytics in reliability
  • failure analysis
  • maintenance optimization

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Published Papers (2 papers)

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Research

22 pages, 3280 KB  
Article
A Novel Scenario-Based Comparative Framework for Short- and Medium-Term Solar PV Power Forecasting Using Deep Learning Models
by Elif Yönt Aydın, Kevser Önal, Cem Haydaroğlu, Heybet Kılıç, Özal Yıldırım, Oğuzhan Katar and Hüseyin Erdoğan
Appl. Sci. 2025, 15(24), 12965; https://doi.org/10.3390/app152412965 - 9 Dec 2025
Viewed by 296
Abstract
Accurate short- and medium-term forecasting of photovoltaic (PV) power generation is vital for grid stability and renewable energy integration. This study presents a comparative scenario-based approach using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) models trained with [...] Read more.
Accurate short- and medium-term forecasting of photovoltaic (PV) power generation is vital for grid stability and renewable energy integration. This study presents a comparative scenario-based approach using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) models trained with one year of real-time meteorological and production data from a 250 kWp grid-connected PV system located at Dicle University in Diyarbakır, Southeastern Anatolia, Turkey. The dataset includes hourly measurements of solar irradiance (average annual GHI 5.4 kWh/m2/day), ambient temperature, humidity, and wind speed, with missing data below 2% after preprocessing. Six forecasting scenarios were designed for different horizons (6 h to 1 month). Results indicate that the LSTM model achieved the best performance in short-term scenarios, reaching R2 values above 0.90 and lower MAE and RMSE compared to CNN and GRU. The GRU model showed similar accuracy with faster training time, while CNN produced higher errors due to the dominant temporal nature of PV output. These results align with recent studies that emphasize selecting suitable deep learning architectures for time-series energy forecasting. This work highlights the benefit of integrating real local meteorological data with deep learning models in a scenario-based design and provides practical insights for regional grid operators and energy planners to reduce production uncertainty. Future studies can improve forecast reliability by testing hybrid models and implementing real-time adaptive training strategies to better handle extreme weather fluctuations. Full article
(This article belongs to the Special Issue Maintenance and Reliability Engineering: Latest Advances and Prospects)
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60 pages, 2454 KB  
Article
Multidimensional Maintenance Maturity Modeling: Fuzzy Predictive Model and Case Study on Ensuring Operational Continuity Under Uncertainty
by Lech Bukowski and Sylwia Werbinska-Wojciechowska
Appl. Sci. 2025, 15(22), 12236; https://doi.org/10.3390/app152212236 - 18 Nov 2025
Viewed by 459
Abstract
Ensuring operational continuity in modern industrial systems requires maintenance strategies that are both mature and adaptive to uncertainty. This study introduces and validates the Integrated Maintenance Maturity Model (IMMM), a multidimensional framework that combines reliability, safety, resilience, flexibility, and sustainability into a structured [...] Read more.
Ensuring operational continuity in modern industrial systems requires maintenance strategies that are both mature and adaptive to uncertainty. This study introduces and validates the Integrated Maintenance Maturity Model (IMMM), a multidimensional framework that combines reliability, safety, resilience, flexibility, and sustainability into a structured maturity assessment approach. Building on the conceptual foundations of maintenance maturity modeling, the IMMM is enhanced with fuzzy logic to address uncertainty, incorporate expert knowledge, and enable nuanced evaluations. A fuzzy inference system based on Mamdani logic was developed to integrate linguistic variables, apply rule-based reasoning, and defuzzify results into maturity scores. The model also includes additional parameters, such as technology adaptability and resource efficiency, to reflect real-world operational complexity. The applicability of the proposed framework was demonstrated through a case study in the automotive sector, where the fuzzy IMMM identified maturity gaps, supported decision-making, and provided strategic recommendations for advancing maintenance practices. Results confirm the model’s effectiveness in enhancing system dependability, adaptability, and sustainability under uncertainty. This work contributes to the development of predictive, uncertainty-aware maintenance maturity models and offers a practical tool for organizations seeking to strengthen operational resilience while aligning with long-term sustainability goals. Full article
(This article belongs to the Special Issue Maintenance and Reliability Engineering: Latest Advances and Prospects)
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