Advanced Signal Processing Technologies for Mechanical Fault Diagnosis in Mechatronics and Intelligent Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 19 March 2026 | Viewed by 194
Special Issue Editors
Interests: fault detection; induction motor; vibration analysis; current control; machine vector control; fuzzy control; programmable control; pulse-width modulation; servomotors; two-term control; failure analysis; fault diagnosis; transfer learning
Interests: applied mechanics; robotics; design engineering; physical rehabilitation; engineering; mechatronics
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Interests: data mining; artificial neural network; artificial intelligence
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Special Issue Information
Dear Colleagues,
Mechanical fault diagnosis plays a crucial role in ensuring the reliability, safety, and efficiency of industrial systems, including electric drives, mechatronic assemblies, and collaborative robots. The increasing demand for predictive maintenance, coupled with the complexity of modern machinery, has led to rapid advances in signal processing, including intelligent, hybrid, and data-driven techniques. Advanced signal processing has become a key enabler for the accurate detection, localization, and classification of mechanical faults, even under nonstationary, noisy, and fault-tolerant conditions.
This Special Issue aims to bring together recent developments and applications of advanced signal processing methods for mechanical fault diagnosis across various engineering domains. It aligns with the scope of Electronics, promoting interdisciplinary solutions in intelligent systems, signal analysis, and fault-tolerant design. We welcome contributions that demonstrate theoretical advancements, practical implementations, and industrial case studies.
In this Special Issue, original research articles and reviews are welcome.
Topics of interest include, but are not limited to, the following:
- Time–frequency and wavelet-based fault detection;
- Machine learning and deep learning in vibration and acoustic signal analysis;
- Fuzzy logic and hybrid intelligent diagnostic systems;
- Fault-tolerant control and monitoring;
- Signal processing for electric drives and rotating machinery;
- Sensor fusion techniques for diagnostic accuracy;
- AI-based prognostics and health management (PHM);
- Real-time diagnostic systems for industrial applications;
- Feature extraction and selection in noisy environments.
Dr. Siarhei Autsou
Dr. Alberto Borboni
Dr. Olga Dunajeva
Guest Editors
Manuscript Submission Information
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Keywords
- fault diagnosis
- signal processing
- data analysis
- mechanical variables measurement
- intelligent systems
- fuzzy logic
- fault-tolerant control
- machine learning algorithms
- industrial control
- predictive maintenance
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