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Symmetry
Special Issues
Symmetry in Fault Detection, Diagnosis, and Prognostics
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Symmetry in Fault Detection, Diagnosis, and Prognostics

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 571

Special Issue Editors

Prof. Dr. Arthur Henrique de Andrade Melani

E-Mail Website
Guest Editor
Department of Mechatronics and Mechanical Systems Engineering, University of Sao Paulo, Sao Paulo 05508-030, Brazil
Interests: fault detection and diagnosis; reliability-based maintenance management; artificial intelligence applied to maintenance; reliability analysis
Prof. Dr. Renan Favarão da Silva

E-Mail Website
Guest Editor
Department of Production Engineering, University of Sao Paulo, Sao Paulo 05508-030, Brazil
Interests: fault detection and diagnosis; machine learning for maintenance; decision support methods; Industry 4.0 and digital technologies

Special Issue Information

Dear Colleagues,

The study of symmetry plays a crucial role in fault detection, diagnosis, and prognostics across various engineering systems. Many mechanical, electrical, and industrial system failures arise from disruptions in expected symmetrical patterns, which can be effectively analyzed using advanced artificial intelligence, machine learning, and signal processing techniques. This Special Issue aims to explore novel methodologies and applications that leverage the principles of symmetry to enhance fault detection accuracy, optimize diagnostic strategies, and improve predictive maintenance frameworks. Contributions related to theoretical advancements, experimental validations, and real-world case studies are welcome. Topics of interest include, but are not limited to, AI-driven fault detection, vibration and acoustic analysis, sensor fusion for diagnostics, and physics-informed machine learning for prognostics.

We invite researchers and practitioners to contribute their latest findings to this Special Issue; we hope to foster innovation in maintenance engineering, reliability analysis, and intelligent fault management.

Prof. Dr. Arthur Henrique de Andrade Melani
Prof. Dr. Renan Favarão da Silva
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

  • symmetry in fault detection
  • symmetry in system degradation
  • AI-based fault diagnosis
  • predictive maintenance
  • signal processing for fault detection
  • physics-informed machine learning
  • vibration analysis
  • industrial diagnostics
  • sensor fusion
  • reliability engineering

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

24 pages, 2645 KB  
Article
Group-Theoretic Bilateral Symmetry Analysis for Automotive Steering Systems: A Physics-Informed Deep Learning Framework for Symmetry-Breaking Fault Pattern Recognition
by Shidian Ma and Bingao Jia
Symmetry 2025, 17(9), 1496; https://doi.org/10.3390/sym17091496 - 9 Sep 2025
Viewed by 304
Abstract
Modern automotive steering systems exhibit inherent bilateral symmetry characteristics that can be mathematically described using group theory. When component failures occur, these systems experience quantifiable symmetry-breaking patterns that serve as diagnostic indicators. This research presents an approach that combines group-theoretic principles with machine [...] Read more.
Modern automotive steering systems exhibit inherent bilateral symmetry characteristics that can be mathematically described using group theory. When component failures occur, these systems experience quantifiable symmetry-breaking patterns that serve as diagnostic indicators. This research presents an approach that combines group-theoretic principles with machine learning for automotive steering system fault diagnosis. The study introduces a physics-informed neural network architecture that leverages the mathematical structure of bilateral symmetry for enhanced fault detection capabilities. Through systematic analysis of eight distinct fault categories including sensor malfunctions, actuator degradation, control system failures, and mechanical wear patterns, the proposed framework demonstrates that symmetry-breaking signatures provide reliable diagnostic features. The framework integrates symmetric convolutional operations with transformer-based attention mechanisms, optimized through physics-constrained particle swarm algorithms. Experimental validation using both simulation data (12,500 scenarios) and physical test bench measurements shows classification accuracy of 94.2% compared to traditional CNN-LSTM (86.2%), SVM (78.9%), and Random Forest (82.7%) approaches. The bilateral symmetry analysis achieves 91.8% sensitivity for fault detection in controlled laboratory environments. These results establish the practical viability of group-theoretic methods for automotive diagnostics while providing a foundation for condition-based maintenance strategies in intelligent vehicle systems. Full article
(This article belongs to the Special Issue Symmetry in Fault Detection, Diagnosis, and Prognostics)
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