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Symmetry
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Symmetry in Fault Detection and Diagnosis for Dynamic Systems
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Symmetry in Fault Detection and Diagnosis for Dynamic Systems

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 2307

Special Issue Editors

Prof. Dr. Jose A. Ruz-Hernandez

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Guest Editor
Facultad de Ingenieria, Universidad Autonoma del Carmen, Ciudad del Carmen 24180, Campeche, Mexico
Interests: fault detection and diagnosis; intelligent control; machine learning; neuro-fuzzy systems; real-time control applications
Prof. Dr. Alma Y. Alanis

E-Mail Website
Guest Editor
Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), University of Guadalajara, Guadalajara 44330, Mexico
Interests: intelligent control; discrete-time nonlinear systems; artificial neural networks; applications to electromechanical systems; biomedical systems; smart grids
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jose-Luis Rullan-Lara

E-Mail Website
Guest Editor
Facultad de Ingenieria, Universidad Autonoma del Carmen, Ciudad del Carmen 24180, Campeche, Mexico
Interests: fault detection and diagnosis; bond graph modelling; unmanned systems; linear and nonlinear control
Prof. Dr. Larbi Djilali

E-Mail Website
Guest Editor
TecNM Chihuahua, División de Estudios de Posgrado e Investigación, Chihuahua 31310, México
Interests: automatic control; power generation; intelligent control; microgrid control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to increasing demands on the reliability and safety of technical processes, multiple fault detection and diagnosis methodologies have been proposed in the literature, broadly divided into model-based techniques, knowledge-based methods, and empirical or signal processing techniques. Faults can occur at any instant in dynamic systems, and in many cases can be generated by the drift of one or multiple parameters of the dynamic system. These changes can be useful to compare healthy and faulty systems when applying different approaches and methodologies.

In this context, this Special Issue aims to highlight both academic and real advancements in fault detection and diagnosis applications for dynamic systems, using conventional and artificial intelligence advanced techniques that emphasize symmetry. Here, symmetry plays an important role in the following ways: data for deep learning; data for machine learning, fault feature extraction or matching in terms of symmetry, fault detection or matching in terms of symmetry, and data segmentation and classification, among others.

Prof. Dr. Jose A. Ruz-Hernandez
Prof. Dr. Alma Y. Alanis
Prof. Dr. Jose-Luis Rullan-Lara
Prof. Dr. Larbi Djilali
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. Symmetry 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 detection and diagnosis
  • artificial intelligence
  • symmetry
  • dynamic systems

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Published Papers (4 papers)

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Research

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40 pages, 1103 KB  
Article
Modified Soft Margin Optimal Hyperplane Algorithm for Support Vector Machines Applied to Fault Patterns and Disease Diagnosis
by Mario Antonio Ruz Canul, Jose A. Ruz-Hernandez, Alma Y. Alanis, Juan Carlos Gonzalez Gomez and Jorge Gálvez
Symmetry 2025, 17(10), 1749; https://doi.org/10.3390/sym17101749 - 16 Oct 2025
Abstract
This paper introduces a modified soft margin optimal hyperplane (MSMOH) algorithm, which enhances the linear separating properties of support vector machines (SVMs) by placing higher penalties on large misclassification errors. This approach improves margin symmetry in both balanced and asymmetric data distributions. The [...] Read more.
This paper introduces a modified soft margin optimal hyperplane (MSMOH) algorithm, which enhances the linear separating properties of support vector machines (SVMs) by placing higher penalties on large misclassification errors. This approach improves margin symmetry in both balanced and asymmetric data distributions. The research is divided into two main stages. The first stage evaluates MSMOH for synthetic data classification and its application in heart disease diagnosis. In a cross-validation setting with unknown data, MSMOH demonstrated superior average performance compared to the standard soft margin optimal hyperplane (SMOH). Performance metrics confirmed that MSMOH maximizes the margin and reduces the number of support vectors (SVs), thus improving classification performance, generalization, and computational efficiency. The second stage applies MSMOH as a novel synthesis algorithm to design a neural associative memory (NAM) based on a recurrent neural network (RNN). This NAM is used for fault diagnosis in fossil electric power plants. By promoting more symmetric decision boundaries, MSMOH increases the accurate convergence of 1024 possible input elements. The results show that MSMOH effectively designs the NAM, leading to better performance than other synthesis algorithms like perceptron, optimal hyperplane (OH), and SMOH. Specifically, MSMOH achieved the highest number of converged input elements (1019) and the smallest number of elements converging to spurious memories (5). Full article
(This article belongs to the Special Issue Symmetry in Fault Detection and Diagnosis for Dynamic Systems)
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17 pages, 3603 KB  
Article
A Fault Diagnosis Method for the Train Communication Network Based on Active Learning and Stacked Consistent Autoencoder
by Yueyi Yang, Haiquan Wang, Xiaobo Nie, Shengjun Wen and Guolong Li
Symmetry 2025, 17(10), 1622; https://doi.org/10.3390/sym17101622 - 1 Oct 2025
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Abstract
As a critical component of rail travel, the train communication network (TCN) is an integrated central platform that is used to realize the train control, condition monitoring, and data transmission, whose failure will disrupt the symmetry of TCN topology and endanger the security [...] Read more.
As a critical component of rail travel, the train communication network (TCN) is an integrated central platform that is used to realize the train control, condition monitoring, and data transmission, whose failure will disrupt the symmetry of TCN topology and endanger the security of rail trains. To enhance the reliability of TCN, an intelligent fault diagnosis method is proposed based on active learning (AL) and a stacked consistent autoencoder (SCAE), which is capable of building a competitive classifier with a limited amount of labeled training samples. SCAE can learn better feature presentations from electrical multifunction vehicle bus (MVB) signals by reconstructing the same raw input data layer by layer in the unsupervised feature learning phase. In the supervised fine-tuning phase, a deep AL-based fault diagnosis framework is proposed, and a dynamic fusion AL method is presented. The most valuable unlabeled samples are selected for labeling and training by considering uncertainty and similarity simultaneously, and the fusion weight is dynamically adjusted at the different training stages. A TCN experimental platform is constructed, and experimental results show that the proposed method achieves better performance under three different metrics with fewer labeled samples compared to the state-of-the-art methods; it is also symmetrically valid in class-imbalanced data. Full article
(This article belongs to the Special Issue Symmetry in Fault Detection and Diagnosis for Dynamic Systems)
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20 pages, 4960 KB  
Article
A Fault Diagnosis Method for Planetary Gearboxes Using an Adaptive Multi-Bandpass Filter, RCMFE, and DOA-LSSVM
by Xin Xia, Aiguo Wang and Haoyu Sun
Symmetry 2025, 17(8), 1179; https://doi.org/10.3390/sym17081179 - 23 Jul 2025
Viewed by 379
Abstract
Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating [...] Read more.
Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article
(This article belongs to the Special Issue Symmetry in Fault Detection and Diagnosis for Dynamic Systems)
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Review

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24 pages, 1377 KB  
Review
Statistical Analysis and Mechanisms of Aircraft Electrical Power System Failures Under Redundant Symmetric Architecture: A Review
by Zhaoyang Zeng, Jinkai Wang, Qingyu Zhu, Changqi Qu and Xiaochun Fang
Symmetry 2025, 17(8), 1341; https://doi.org/10.3390/sym17081341 - 17 Aug 2025
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Abstract
The aircraft power supply system plays a crucial role in maintaining the stability and safety of airborne avionics. With the evolution toward more electric and all-electric aircraft, its architecture increasingly adopts symmetrical configurations, such as dual-redundant paths and three-phase balanced outputs. However, these [...] Read more.
The aircraft power supply system plays a crucial role in maintaining the stability and safety of airborne avionics. With the evolution toward more electric and all-electric aircraft, its architecture increasingly adopts symmetrical configurations, such as dual-redundant paths and three-phase balanced outputs. However, these symmetry-based designs are often disrupted by diverse fault mechanisms encountered in complex operational environments. This review contributes a comprehensive and structured analysis of how such fault events lead to symmetry-breaking phenomena across different subsystems, including generators, converters, controllers, and distribution networks. Unlike previous reviews that treat faults in isolation, this study emphasizes the underlying physical mechanisms and hierarchical fault propagation characteristics, revealing how structural coupling and multi-physics interactions give rise to failure modes. The paper concludes by outlining future research directions in symmetry-aware fault modeling and intelligent maintenance strategies, aiming to address the growing complexity and reliability demands of next-generation aircraft. Full article
(This article belongs to the Special Issue Symmetry in Fault Detection and Diagnosis for Dynamic Systems)
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