Advanced Machine Condition Monitoring and Fault Diagnosis
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 20
Special Issue Editor
Interests: the detection of defects in rotating mechanical elements, specifically in shafts, participating in several projects in the field of railway industry
Special Issue Information
Dear Colleagues,
This Special Issue focuses on recent advances in machine condition monitoring and fault diagnosis, which are critical for ensuring the reliability, safety, and efficiency of industrial systems. With the increasing integration of intelligent systems, sensor technologies, and data-driven approaches, condition monitoring has evolved into a powerful tool for predictive maintenance and early fault detection.
We welcome the submission of high-quality articles that explore innovative methodologies, algorithms, and applications in this field. The scope of this Special Issue includes, but is not limited to, advanced signal processing techniques, machine learning and deep learning for fault diagnosis, non-stationary and non-linear system analysis, sensor fusion, remaining useful life (RUL) prediction, digital twin applications, and real-time monitoring systems. Contributions may address mechanical, electrical, or hybrid systems in the industrial, transportation, or energy sectors.
In addition, papers may be theoretical, computational, or experimental in nature, and should provide clear insights into how the proposed methods improve upon existing approaches. Case studies and validation with real-world data are also encouraged.
This Special Issue aims to bring together researchers and practitioners from academia and industry to foster interdisciplinary collaboration and push the boundaries of condition monitoring and fault diagnosis technologies.
Dr. Gómez María Jesús
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. 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
- condition monitoring
- fault diagnosis
- predictive maintenance
- machine learning
- signal processing
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