Big Data Analytics and Deep Learning for Predictive Maintenance

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 Sciences. 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-Ortner
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 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.