Abstract:

Experimental Modal Analysis is a fundamental procedure in mechanical engineering for understanding the dynamic behavior of structures through impulsive excitation, specifically by using an instrumented impact hammer. In educational laboratory settings, this manual procedure presents a pedagogical challenge: novice students struggle to identify measurement errors from abstract graphical data displayed on monitors, creating a dependency on constant expert supervision. This dependency transforms what could be an active, embodied learning experience into passive procedural imitation discouraging students from developing the psychomotor skills and expert cognitive schemas necessary for autonomous practice.

This work introduces the Unified Screen Monitoring Application (USMA), a novel, lightweight, fully portable software tool that addresses this pedagogical gap through non-invasive computer vision technology. USMA provides real-time, immediate feedback on measurement quality by monitoring the proprietary data acquisition software's screen output, isolating the Frequency Response Function curve via Hue-Saturation-Value color thresholding, reconstructing its signal vector, and analyzing spectral characteristics using Fast Fourier Transform algorithms. The system currently detects the most common novice error: the "double hit," where rapidly successive impacts introduce high-frequency periodic distortion into the force spectrum.

The pedagogical innovation lies in USMA's function as a concurrent sensorimotor feedback system. Drawing on motor learning theory, specifically the Fitts and Posner three-stage skill acquisition model, USMA provides appropriate "Knowledge of Performance" feedback that novices in the Cognitive Stage require. By delivering immediate, explicit identification of the specific motor pattern error (e.g., "DOUBLE HIT"), the system closes the broken action-perception loop that may trap students in dependency. The immediacy of the feedback is critical since it enables the learner to correctly attribute an external signal to the internal kinesthetic feel of their physical action preventing the reinforcement of incorrect motor patterns whilst accelerating progression to the Associative Stage of skill development.

Beyond educational applications, USMA's principles extend to industrial quality control. The non-invasive, vision-based approach that doesn't require hardware modification or API access offers a scalable solution for automated measurement quality supervision in manufacturing environments, ensuring robust and repeatable practices.

Keywords:

Experimental Modal Analysis, Engineering Education, Real-Time Feedback, Computer Vision, Technology-Enhanced Learning.