Deep Learning-Based Intelligent 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 July 2026 | Viewed by 9
Special Issue Editors
Interests: dynamic modeling; signal processing; machine learning; fault diagnosis
Interests: dynamic modeling; signal processing; machine learning; fault diagnosis
Interests: dynamic modeling; signal processing; machine learning; fault diagnosis
Special Issue Information
Dear Colleagues,
With the rapid advancement of artificial intelligence and industrial digitalization, deep learning has emerged as a powerful tool for intelligent fault diagnosis across various engineering systems. This Special Issue aims to showcase cutting-edge research on deep learning methodologies applied to fault detection, diagnosis, and prognosis in industrial systems such as wind turbines, pumps, fans, and robots. We invite original research articles and review papers addressing the following (but not limited to) topics:
- Novel deep neural architectures or new AI methods for fault diagnosis.
- Explainable AI and interpretability in fault diagnosis systems.
- Multimodal sensor fusion and feature learning approaches.
- Transfer learning and domain adaptation for cross-condition diagnosis.
- Continual learning for fault diagnosis.
- Edge computing and real-time deployment of diagnosis models.
- Hybrid physics-informed deep learning models.
- Benchmark datasets for machinery fault diagnosis.
- Case studies in manufacturing, energy, transportation, and other industrial applications.
This Special Issue seeks to bring together researchers from academia and industry to share innovative solutions that push the boundaries of intelligent maintenance systems. Submissions should demonstrate both methodological novelty and practical relevance, with rigorous experimental validation on real-world or realistic synthetic datasets.
Dr. Meng Rao
Dr. Xingkai Yang
Dr. Yaqiang Jin
Dr. Zheng Cao
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
- fault diagnosis
- deep learning
- artificial intelligence
- industrial systems
- machinery
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