Advanced Signal Processing Technologies for Mechanical Fault Diagnosis in Mechatronics and Intelligent Systems

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

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