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Symmetry
Special Issues
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 629

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 (1 paper)

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Research

23 pages, 4960 KiB  
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 36
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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