Fault Diagnosis Technologies for Intelligent Engineering Systems
A special issue of Technologies (ISSN 2227-7080).
Deadline for manuscript submissions: 25 September 2026 | Viewed by 218
Special Issue Editors
Interests: data-driven and hybrid fault diagnosis; digital twins; multimodal learning; kernel-based methods; uncertainty-aware modeling; and intelligent control of renewable energy systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Fault diagnosis is essential for ensuring the safety, reliability, and efficiency of modern engineering systems, particularly in aerospace, industrial automation, transportation, and renewable energy domains. Traditional model-based fault diagnosis (MBFD) techniques leverage physical laws and analytical redundancy, providing interpretability and theoretical rigor, but their performance may deteriorate in nonlinear or uncertain conditions. Data-driven fault diagnosis (DDFD) approaches, powered by advances in sensing, machine learning, and deep learning, capture complex system behaviors from operational data but often require large datasets and may lack transparency. This Special Issue focuses on hybrid methodologies that integrate MBFD and DDFD, achieving enhanced robustness, interpretability, adaptability, and real-time performance. Contributions on hybrid residual generation, digital twins, physics-informed neural networks, trustworthy AI, multimodal sensing, and intelligent fault diagnosis across engineering systems are welcome. The aim is to gather original research, reviews, and case studies demonstrating scalable, explainable, and industry-ready hybrid fault diagnosis solutions. This Special Issue aims to address these challenges by bringing together high-quality research contributions on intelligent, data-driven, and trustworthy methodologies for energy system optimization, monitoring, and fault diagnosis, with particular emphasis on renewable energy systems and power networks.
This Special Issue welcomes original research articles, review papers, and case studies covering, but not limited to, the following topics:
- Hybrid fault diagnosis.
- Model-based fault diagnosis (MBFD).
- Data-driven fault diagnosis (DDFD).
- Physics-informed neural networks.
- Digital twins.
- Machine learning.
- Trustworthy AI.
- Condition monitoring.
- Fault detection and isolation (FDI).
- Renewable energy systems.
- Kalman filtering.
- Multimodal sensing.
Dr. Majdi Mansouri
Dr. Mahamadou Abdou-Tankari
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 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. Technologies 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 1800 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
- model-based fault diagnosis (MBFD)
- data-driven fault diagnosis (DDFD)
- hybrid diagnostic methods
- physics-informed neural networks (PINNs)
- digital twins
- residual generation
- trustworthy AI
- explainable AI
- multimodal sensing
- intelligent fault diagnosis
- condition monitoring
- renewable energy systems
- power systems
- energy system optimization
- anomaly detection
- real-time fault detection
- robust and interpretable diagnostics
- machine learning
- deep learning
- industry-ready solutions.
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