Intelligent Predictive Maintenance and Machine Condition Monitoring
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 6
Special Issue Editors
Interests: train inspection and fault diagnosis technology
Interests: failure analysis; fault diagnosis; higher-order statistics
Special Issue Information
Dear Colleagues,
The growing integration of intelligence and automation in high-end industrial equipment—including high-speed trains, wind turbines, engines, gas turbines, compressors, and machine tools—has heightened the importance of operational safety and reliability among both academic and industrial communities. Intelligent predictive maintenance and machine condition monitoring have emerged as essential approaches to sustain operational continuity, lower maintenance expenditures, and increase overall productivity. In response, scholars and engineers are actively developing sophisticated intelligent solutions to enable precise fault prediction, performance assessment, and maintenance decision-making. Advances in sensing systems, IoT infrastructure, and large-scale data processing have greatly facilitated the acquisition and analysis of equipment status data, establishing new opportunities for data-driven monitoring and prognostic methods. Accordingly, this Special Issue is dedicated to presenting cutting-edge research on condition monitoring, fault diagnosis, and predictive maintenance of high-end equipment through the application of advanced signal processing and artificial intelligence techniques.
This Special Issue welcomes the submission of high-quality original research and review manuscripts that introduce innovative concepts, algorithms, methodologies, and technologies contributing to the evolution of intelligent maintenance frameworks. The scope of this Special Issue includes, but is not limited to, the following topics:
- Intelligent fault prediction and condition management in high-speed trains, wind turbines, engines, gas turbines, compressors, and machine tools;
- Monitoring and maintenance strategies for critical components such as bearings, gears, and rotors;
- Emerging sensing and data collection techniques for equipment condition assessment;
- Artificial intelligence, machine learning, and deep learning applications in prognostics and maintenance;
- Signal processing and feature analysis methods for fault detection and performance tracking;
- Implementation of digital twin and cyber–physical systems in health management.
We invite researchers and practitioners to contribute their original work to this Special Issue.
Dr. Cai Yi
Dr. Qiuyang Zhou
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 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. Machines is an international peer-reviewed open access monthly 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
- intelligent fault prediction
- machine condition monitoring
- maintenance
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