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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 September 2025 | Viewed by 6224

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 (3 papers)

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Research

17 pages, 3450 KiB  
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 373
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 KiB  
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 2 | Viewed by 1251
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 KiB  
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 2 | Viewed by 4000
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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