Building a Machine Condition Monitoring Device as a Hands-on Teaching Tool: An Educational Experience for Students of Engineering and Seismology

This study introduces VibroScan, a low-cost, open-source vibration monitoring system developed using microelectromechanical systems-based accelerometers and ESP32 microcontrollers, tailored for educational and field applications. The system enables real-time triaxial acceleration data acquisition, frequency-domain anomaly detection via fast Fourier transform and machine learning algorithms, and remote monitoring through a message queuing telemetry transport-based web interface. Designed and tested on a metal casting machine, the system successfully identified dominant spectral behaviors and operational anomalies during casting cycles. As part of the hands-on engineering experience, students contributed to the design of the sensor enclosure, system logo, and a responsive web dashboard built with HTML, JavaScript, and Chart.js. These tasks enhanced their understanding of not only embedded systems and signal processing, but also product development and system integration. Comparative analysis of Z score and long-short term memory (LSTM)-based models revealed that while both methods detect spectral anomalies effectively, LSTM demonstrated superior sensitivity to dynamic frequency shifts. Beyond industrial diagnostics, this study is also of value in seismology with for example implications of machine health monitoring in earthquake early warning contexts. Many industrial machines suffer significant economic losses during seismic events due to undetected resonance or structural failure. Deploying smart sensors like VibroScan in critical facilities enables dual function monitoring continuous machinery assessment and preseismic anomaly tracking offering a practical step toward infrastructure resilience. VibroScan exemplifies how accessible hardware and modern signal analytics can be leveraged in Science, Technology, Engineering, and Mathematics curricula for students of engineering and seismology to bridge theoretical learning and real world problem solving. It provides students with direct exposure to data-driven maintenance systems, Internet of Things architectures, and the societal impact of smart instrumentation key competencies for future engineers and researchers in seismology, structural monitoring, and beyond.