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Applied Sciences
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Big Data Analytics and Deep Learning for Predictive Maintenance
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Big Data Analytics and Deep Learning for Predictive Maintenance

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 14567

Special Issue Editor

Dr. Elaheh Momeni

E-Mail Website
Guest Editor
Department of Computer Science, University of Applied Science Technikum Vienna, 1200 Vienna, Austria
Interests: artificial intelligence; machine learning; information retrieval; predictive maintenance; NLP

Special Issue Information

Dear Colleagues,

The integration of Big Data Analytics and Deep Learning is revolutionizing predictive maintenance, a critical area of focus for industries aiming to enhance operational efficiency and reduce downtime. As industrial systems become increasingly complex, the ability to predict and prevent equipment failures through advanced data-driven approaches is essential. Predictive maintenance not only minimizes maintenance costs but also improves safety and system reliability. Recently, the emergence of Deep Learning and Generative AI has further enriched this field, offering new possibilities for model enhancement, data augmentation, and the development of sophisticated simulations. These innovations are paving the way for more accurate, adaptive, and resilient maintenance strategies.

We are pleased to invite you to contribute to our upcoming Special Issue on “Big Data Analytics and Deep Learning for Predictive Maintenance” in the journal Applied Science. This Special Issue seeks to compile cutting-edge research that explores the synergy between Big Data, Deep Learning, and Generative AI in the context of predictive maintenance. We are particularly interested in submissions that showcase how these technologies can be harnessed to improve predictive accuracy, optimize maintenance workflows, and introduce innovative approaches to equipment monitoring and fault prevention.

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

  • Development of Deep Learning models for predictive maintenance;
  • Integration of IoT and sensor data with Big Data Analytics for predictive maintenance;
  • Case studies on the application of Big Data and Deep Learning in maintenance optimization;
  • Generative AI techniques for data augmentation and model improvement in predictive maintenance;
  • Simulation and digital twin technologies using Generative AI for predictive maintenance;
  • Machine learning and Deep Learning methods for equipment failure prediction;
  • Data fusion and integration techniques to enhance predictive accuracy;
  • Economic impact and cost–benefit analysis of predictive maintenance strategies;
  • Industry-specific applications of Big Data, Deep Learning, and Generative AI in manufacturing, energy, transportation, and healthcare.

The inclusion of Generative AI within the scope of predictive maintenance research represents a significant step forward, offering new insights and capabilities for tackling real-world maintenance challenges. This Special Issue aims to highlight these advancements and foster a deeper understanding of how these cutting-edge technologies can be applied effectively.

We look forward to receiving your contributions and sharing the latest advancements in this dynamic and rapidly evolving field with the broader scientific community.

Thank you for considering this opportunity to contribute to our Special Issue.

Dr. Elaheh Momeni
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. 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

  • predictive maintenance
  • big data analytics
  • deep learning
  • generative AI
  • equipment failure prediction
  • machine learning for maintenance
  • IoT in predictive maintenance
  • data fusion techniques
  • AI-driven maintenance optimization
  • condition monitoring
  • sensor data integration

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Published Papers (6 papers)

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Research

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24 pages, 1370 KB  
Article
Quantifying Operational Uncertainty in Landing Gear Fatigue: A Hybrid Physics–Data Framework for Probabilistic Remaining Useful Life Estimation of the Cessna 172 Main Gear
by David Gerhardinger, Karolina Krajček Nikolić and Anita Domitrović
Appl. Sci. 2025, 15(20), 11049; https://doi.org/10.3390/app152011049 - 15 Oct 2025
Viewed by 174
Abstract
Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid physics–data framework to quantify operational-load-driven uncertainty in the main [...] Read more.
Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid physics–data framework to quantify operational-load-driven uncertainty in the main landing gear strut of a Cessna 172. High-fidelity finite-element strain–life simulations were combined with a quadratic Ridge surrogate and a two-layer bootstrap to generate full probabilistic RUL distributions. The surrogate mapped five mass-and-balance inputs (fuel, front seats, rear seats, forward and aft baggage) to per-flight fatigue damage with high accuracy (R2 = 0.991 ± 0.013). At the same time, ±3% epistemic confidence bands were attached via resampling. Borgonovo’s moment-independent Δ indices were applied to incremental damage (ΔD) in this context, revealing front-seat mass as the dominant driver of fatigue variability (Δ = 0.502), followed by fuel (0.212), rear seats (0.199), forward baggage (0.141), and aft baggage (0.100). The resulting RUL distribution spanned 9 × 104 to >2 × 106 cycles, with a fleet average of 0.41 million cycles (95% CI: 0.300–0.530 million). These results demonstrate that operational levers—crew assignment, fuel loading, and baggage placement—can significantly extend strut life. Although demonstrated on a specific training fleet dataset, the methodological framework is, in principle, transferable to other aircraft or mission types. However, this would require developing a new, component-specific finite element model and retraining the surrogate using a representative set of mass and balance records from the target fleet. Full article
(This article belongs to the Special Issue Big Data Analytics and Deep Learning for Predictive Maintenance)
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33 pages, 4811 KB  
Article
Enhancing the Prediction of Episodes of Aggression in Patients with Dementia Using Audio-Based Detection: A Multimodal Late Fusion Approach with a Meta-Classifier
by Ioannis Galanakis, Rigas Filippos Soldatos, Nikitas Karanikolas, Athanasios Voulodimos, Ioannis Voyiatzis and Maria Samarakou
Appl. Sci. 2025, 15(10), 5351; https://doi.org/10.3390/app15105351 - 10 May 2025
Cited by 1 | Viewed by 985
Abstract
This study presents an enhancement in the prediction of aggressive outbursts in dementia patients from our previous work, by integrating audio-based violence detection into our previous visual-based aggressive body movement detections. By combining audio and visual information, we aim to further enhance the [...] Read more.
This study presents an enhancement in the prediction of aggressive outbursts in dementia patients from our previous work, by integrating audio-based violence detection into our previous visual-based aggressive body movement detections. By combining audio and visual information, we aim to further enhance the model’s capabilities and make it more suitable for real-world scenario applications. This current work utilizes an audio dataset, containing various audio segments capturing vocal expressions during aggressive and non-aggressive scenarios. Various noise-filtering techniques were performed on the audio files using Mel-frequency cepstral coefficients (MFCCs), frequency filtering, and speech prosody to extract clear information from the audio features. Furthermore, we perform a late fusion rule to merge the predictions of the two models into a unified trained meta-classifier to determine the further improvement of the model with the audio integrated into it with a higher aim for a more precise and multimodal approach in detecting and predicting aggressive outburst behavior in patients suffering from dementia. The analysis of the correlations in our multimodal approach suggests that the accuracy of the early detection models is improved, providing a novel proof of concept with the appropriate findings to advance the understanding of aggression prediction in clinical settings and offer more effective intervention tactics from caregivers. Full article
(This article belongs to the Special Issue Big Data Analytics and Deep Learning for Predictive Maintenance)
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17 pages, 3450 KB  
Article
Neural Network Approach for Fatigue Crack Prediction in Asphalt Pavements Using Falling Weight Deflectometer Data
by Bishal Karki, Sayla Prova, Mayzan Isied and Mena Souliman
Appl. Sci. 2025, 15(7), 3799; https://doi.org/10.3390/app15073799 - 31 Mar 2025
Viewed by 1402
Abstract
Fatigue cracking is a major issue in asphalt pavements, reducing their lifespan and increasing maintenance costs. This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. The model is calibrated utilizing Falling Weight Deflectometer (FWD) [...] Read more.
Fatigue cracking is a major issue in asphalt pavements, reducing their lifespan and increasing maintenance costs. This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. The model is calibrated utilizing Falling Weight Deflectometer (FWD) testing data, alongside essential pavement characteristics such as layer thickness, air void percentage, asphalt binder proportion, traffic loads (Equivalent Single Axle Loads or ESALs), and mean annual temperature. By analyzing these factors, the ANN captures complex relationships influencing fatigue cracking more effectively than traditional methods. A comprehensive dataset from the Long-Term Pavement Performance (LTPP) program is used for model training and validation. The ANN’s ability to adapt and recognize patterns enhances its predictive accuracy, allowing for more reliable pavement condition assessments. Model performance is evaluated against real-world data, confirming its effectiveness in predicting fatigue cracking with an overall R2 of 0.9. This study’s findings provide valuable insights for pavement maintenance and rehabilitation planning, helping transportation agencies optimize repair schedules and reduce costs. This research highlights the growing role of AI in pavement engineering, demonstrating how machine learning can improve infrastructure management. By integrating ANN-based predictive analytics, road agencies can enhance decision-making, leading to more durable and cost-effective pavement systems for the future. Full article
(This article belongs to the Special Issue Big Data Analytics and Deep Learning for Predictive Maintenance)
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49 pages, 1608 KB  
Article
Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance
by Leonel Patrício, Leonilde Varela and Zilda Silveira
Appl. Sci. 2025, 15(2), 854; https://doi.org/10.3390/app15020854 - 16 Jan 2025
Cited by 6 | Viewed by 3499
Abstract
This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, [...] Read more.
This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations. Full article
(This article belongs to the Special Issue Big Data Analytics and Deep Learning for Predictive Maintenance)
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24 pages, 1410 KB  
Article
Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis
by Yina Xia, Seong-Yoon Shin and Kwang-Seong Shin
Appl. Sci. 2024, 14(20), 9506; https://doi.org/10.3390/app14209506 - 18 Oct 2024
Cited by 6 | Viewed by 6648
Abstract
This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and [...] Read more.
This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model’s adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences. Full article
(This article belongs to the Special Issue Big Data Analytics and Deep Learning for Predictive Maintenance)
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Review

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31 pages, 3840 KB  
Review
Efficient and Secure GANs: A Survey on Privacy-Preserving and Resource-Aware Models
by Niovi Efthymia Apostolou, Elpida Vasiliki Balourdou, Maria Mouratidou, Eleni Tsalera, Ioannis Voyiatzis, Andreas Papadakis and Maria Samarakou
Appl. Sci. 2025, 15(20), 11207; https://doi.org/10.3390/app152011207 (registering DOI) - 19 Oct 2025
Abstract
Generative Adversarial Networks (GANs) generate synthetic content to support applications such as data augmentation, image-to-image translation, and training models where data availability is limited. Nevertheless, their broader deployment is constrained by limitations in data availability, high computational and energy demands, as well as [...] Read more.
Generative Adversarial Networks (GANs) generate synthetic content to support applications such as data augmentation, image-to-image translation, and training models where data availability is limited. Nevertheless, their broader deployment is constrained by limitations in data availability, high computational and energy demands, as well as privacy and security concerns. These factors restrict their scalability and integration in real-world applications. This survey provides a systematic review of research aimed at addressing these challenges. Techniques such as few-shot learning, consistency regularization, and advanced data augmentation are examined to address data scarcity. Approaches designed to reduce computational and energy costs, including hardware-based acceleration and model optimization, are also considered. In addition, strategies to improve privacy and security, such as privacy-preserving GAN architectures and defense mechanisms against adversarial attacks, are analyzed. By organizing the literature into these thematic categories, the review highlights available solutions, their trade-offs, and remaining open issues. Our findings underline the growing role of GANs in artificial intelligence, while also emphasizing the importance of efficient, sustainable, and secure designs. This work not only concentrates the current knowledge but also sets the basis for future research. Full article
(This article belongs to the Special Issue Big Data Analytics and Deep Learning for Predictive Maintenance)
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