Failure Diagnosis of Complex Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 324
Special Issue Editor
Interests: fault diagnosis of power systems and electrical equipment; safe and stable operation of smart grids and energy Internets; application of artificial intelligence technology in power systems and integrated energy systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modern complex systems, ranging from industrial machinery and power grids to autonomous vehicles and the healthcare network, are increasingly intertwined with nonlinear dynamics, interdependencies, and high uncertainty. While these systems drive technological innovation, their susceptibility to unexpected failures poses significant challenges to reliability, safety, and efficiency. Timely and accurate failure diagnosis is critical yet remains a formidable task due to the interplay of noisy data, model inaccuracies, and emergent behaviors in complex systems.
This Special Issue invites contributions addressing novel theories, methodologies, and applications in failure diagnosis for complex systems. Submissions should emphasize rigorous mathematical foundations, scalability, and real-world applicability. Interdisciplinary insights bridging entropy, nonlinear dynamics, statistical mechanics, and information theory are particularly encouraged. By fostering advancements in fault diagnosis frameworks, this issue aims to enhance robustness in critical infrastructures and accelerate the transition to intelligent, self-diagnosing systems. We welcome original research articles, reviews, and perspectives from academia and industry. Topics of interest include, but are not limited to, the following:
- fault detection and diagnosis methods for complex systems based on information theory and entropy analysis
- data-driven prognostics and health management technology
- condition monitoring and fault location technologies for key equipment in smart grids
- real-time fault diagnosis framework for complex systems driven by digital twins
- deep learning-based multimodal fault feature extraction and classification algorithms
- machine learning/AI-enhanced diagnostics (e.g., deep learning and reinforcement learning)
- application of complex network theory in fault propagation analysis of critical infrastructures
- fault pattern recognition and fault-tolerant control for new energy systems (e.g., wind turbines and photovoltaic arrays)
Prof. Dr. Tao Wang
Guest Editor
Manuscript Submission Information
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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. Entropy 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 2600 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
- information theory
- entropy
- deep learning
- nonlinear dynamics
- fault detection
- fault diagnosis
- fault pattern recognition
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