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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


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Guest Editor
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
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

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. 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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Published Papers (1 paper)

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Research

26 pages, 3112 KiB  
Article
Pre-Warning for the Remaining Time to Alarm Based on Variation Rates and Mixture Entropies
by Zijiang Yang, Jiandong Wang, Honghai Li and Song Gao
Entropy 2025, 27(7), 736; https://doi.org/10.3390/e27070736 - 9 Jul 2025
Viewed by 199
Abstract
Alarm systems play crucial roles in industrial process safety. To support tackling the accident that is about to occur after an alarm, a pre-warning method is proposed for a special class of industrial process variables to alert operators about the remaining time to [...] Read more.
Alarm systems play crucial roles in industrial process safety. To support tackling the accident that is about to occur after an alarm, a pre-warning method is proposed for a special class of industrial process variables to alert operators about the remaining time to alarm. The main idea of the proposed method is to estimate the remaining time to alarm based on variation rates and mixture entropies of qualitative trends in univariate variables. If the remaining time to alarm is no longer than the pre-warning threshold and its mixture entropy is small enough then a warning is generated to alert the operators. One challenge for the proposed method is how to determine an optimal pre-warning threshold by considering the uncertainties induced by the sample distribution of the remaining time to alarm, subject to the constraint of the required false warning rate. This challenge is addressed by utilizing Bayesian estimation theory to estimate the confidence intervals for all candidates of the pre-warning threshold, and the optimal one is selected as the one whose upper bound of the confidence interval is nearest to the required false warning rate. Another challenge is how to measure the possibility of the current trend segment increasing to the alarm threshold, and this challenge is overcome by adopting the mixture entropy as a possibility measurement. Numerical and industrial examples illustrate the effectiveness of the proposed method and the advantages of the proposed method over the existing methods. Full article
(This article belongs to the Special Issue Failure Diagnosis of Complex Systems)
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