Intelligent Algorithms and Signal Processing Techniques for Fault Diagnosis in Mechanical and Electrical Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 764

Special Issue Editors


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Guest Editor
ENAP-RG, CA Sistemas Dinámicos y Control, Departamento de Electromecánica, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Campus San Juan del Río, San Juan del Río 76807, Querétaro, México
Interests: signal processing; machine learning; deep learning; fault diagnosis; electric machines; bio-inspired algorithms; optimization techniques; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Program of Electrical Technology, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
Interests: automatic control; power quality; signal processing; electrical machines; fault diagnosis; smart grids; condition monitoring; renewable energy systems

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Guest Editor
Department of Mechanical, Automotive and Materials Engineering, Ed Lumley Centre for Engineering Innovation, Rm. 2174 CEI, University of Windsor, Windsor, ON N9B 3P4, Canada
Interests: model-based and data-driven fault detection; diagnostics; prognosis; multi-agent systems (satellites, drones, vehicles, etc.); machine learning; intelligent systems; intelligent manufacturing; Industry 5.0; systems and control theory; linear and nonlinear controller/observer design; avionics; sensors; measurement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the growing complexity of mechanical and electrical systems has highlighted the critical need for accurate and efficient fault diagnosis methods. Unexpected failures in such systems can lead to significant economic losses, safety hazards, and reduced operational reliability. Consequently, integrating intelligent algorithms and advanced signal processing techniques has emerged as a powerful approach for addressing these challenges.

This Special Issue will bring together cutting-edge research contributions that explore innovative algorithmic approaches, such as machine learning, optimization, evolutionary computation, and bio-inspired methods, combined with signal processing techniques for analyzing vibration, acoustic, electrical, and other relevant signals. These methods are increasingly applied to early fault detection, condition monitoring, and predictive maintenance across varied domains, including industrial machinery, electrical systems (including renewable energy sources), electrical machines, cyber–physical systems, etc.

We welcome high-quality submissions presenting original research, comprehensive reviews, and novel applications of intelligent algorithms and signal processing for fault diagnosis. Both theoretical developments and practical implementations are of interest. Contributions incorporating modern technologies, such as the Internet of Things (IoT), edge computing, digital twins, and embedded systems, to enhance scalability, connectivity, and real-time performance are especially encouraged. Studies emphasizing noise robustness, interpretability, and hardware–software integration are also welcome.

Dr. Martin Valtierra-Rodriguez
Dr. Maximiliano Bueno-Lopez
Dr. Afshin Rahimi
Guest Editors

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Keywords

  • fault diagnosis
  • intelligent algorithms
  • signal processing
  • condition monitoring
  • predictive maintenance
  • electrical systems
  • renewable energy systems
  • electrical machines
  • mechanical systems
  • industrial machinery
  • cyber–physical systems
  • optimization techniques
  • machine learning
  • bio-inspired computation
  • Internet of things (IoT)
  • edge computing
  • digital twins
  • embedded systems
  • noise robustness

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Published Papers (1 paper)

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Research

18 pages, 4873 KB  
Article
Optimized GRU with Self-Attention for Bearing Fault Diagnosis Using Bayesian Hyperparameter Tuning
by Zongchao Liu, Shuai Teng and Shaodi Wang
Algorithms 2025, 18(9), 576; https://doi.org/10.3390/a18090576 - 12 Sep 2025
Viewed by 435
Abstract
Rolling bearing failures cause significant production downtime and economic losses. Traditional diagnostic methods suffer from low efficiency, suboptimal accuracy, and susceptibility to human subjectivity. To address these limitations, this paper proposes a novel bearing fault diagnosis (BFD) approach leveraging a Gated Recurrent Unit [...] Read more.
Rolling bearing failures cause significant production downtime and economic losses. Traditional diagnostic methods suffer from low efficiency, suboptimal accuracy, and susceptibility to human subjectivity. To address these limitations, this paper proposes a novel bearing fault diagnosis (BFD) approach leveraging a Gated Recurrent Unit (GRU) network. Key contributions include: (1) Employing Bayesian optimization to automate the search for the optimal GRU architecture (layers, hidden units) and hyperparameters (learning rate, batch size, epochs), significantly enhancing diagnostic performance (achieving 97.9% accuracy). (2) Integrating a self-attention mechanism to further improve the GRU’s feature extraction capability from vibration signals, boosting accuracy to 99.6%. (3) Demonstrating the robustness of the optimized GRU with self-attention across varying motor speeds (1772 rpm, 1750 rpm, 1730 rpm), consistently maintaining diagnostic accuracy above 97%. Comparative studies with Bayesian-optimized Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models confirm the superior accuracy (97.9% vs. 95.1% and 90.0%) and faster inference speed (0.27 s) of the proposed GRU-based method. The results validate that the combination of Bayesian optimization, GRU, and self-attention provides an efficient, accurate, and robust intelligent solution for automated BFD. Full article
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