Advanced Dynamic Measurement and Vibration Analysis for Smart Industrial Systems: Novel Sensing Technologies and AI-Driven Diagnostics

Dear Colleagues,

The rapid evolution of sensing technologies, from traditional contact-based methods to sophisticated non-contact optical systems, has enabled unprecedented insights into machine behavior. Three-dimensional laser vibrometry, particle image velocimetry, and high-speed imaging now provide detailed characterization of complex dynamic phenomena that were previously impossible to measure. When combined with artificial intelligence and machine learning algorithms, these technologies enable intelligent pattern recognition, anomaly detection, and predictive analytics that transform raw measurement data into actionable industrial intelligence.

This Special Issue emphasizes the integration of multiple sensing modalities to create comprehensive monitoring systems. Modern industrial applications require the fusion of accelerometer data, optical measurements, strain sensing, and environmental parameters to fully understand system behavior. The challenge lies not only in acquiring high-quality measurements but also in processing, analyzing, and interpreting vast amounts of multi-dimensional data in real-time industrial environments.

Areas of interest focus on vibration and sound/noise and include, but are not limited to, the following:

1. Advanced Dynamic Measurement Techniques :

• Non-invasive sensing methods ;
• Dynamic calibration and real-time uncertainty analysis;
• Vibration and acoustic data acquisition under operational conditions.

2. Vibration and Acoustic Diagnostics:

• Modeling and analysis of vibrations in complex mechanical systems;
• Fault detection and structural health monitoring of machinery;
• Development of digital twins and predictive NVH (Noise, Vibration, Harshness) models.

3. Robotics, Cobots, and Intelligent Mechatronic Systems:

• Vibration and noise diagnostics in mobile robots, manipulators, and collaborative robots;
• Integration of dynamic sensors with control and adaptive systems;
• Vocal communication between cobots and humans;
• Sound generation by robots and cobots.

4. Design Optimization and Vibration Control:

• Isolation strategies, acoustic barriers, and vibration absorbers (e.g., micro-perforated panels);
• Adaptive active and passive vibration control;
• Real-time sensing–control synchronization, including feedback loops.

5. Acoustic Identification and Sound Signal Analysis:

• Noise source detection and localization in machinery and robots;
• Sound-based machine condition classification ;
• Machine learning and AI approaches for acoustic diagnostic modeling.

6. Engineering Precision and Digital Transformation:

• Optimization of sensing systems and data acquisition strategies;
• Multi-physics integration of acoustic, vibration, and geometric measurements;
• Implementation of digital twins, IoT, and cloud-based monitoring platforms.

Dr. Lukasz Scislo
Dr. Nina Szczepanik-Scislo
Guest Editors

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