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Low-Cost IoT-Based Predictive Maintenance Using Vibration.

Peter Kolok1, Michal Hodoň1, Peter Ševčík1

  • 1Department of Technical Cybernetics, Faculty of Management Science and Informatics, University of Žilina, Univerzitna 8215/1, 010 26 Žilina, Slovakia.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary

A new low-cost, IoT system uses vibration and acoustic sensors with machine learning for predictive maintenance. This cost-effective solution achieves over 73% accuracy in detecting faults in small rotating machinery.

Keywords:
ESP32MEMS sensorsacoustic monitoringanomaly detectionpredictive maintenancevibration analysis

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Predictive maintenance reduces costs and improves reliability by anticipating equipment failures.
  • Existing solutions are often too expensive or complex for small rotating machinery.

Purpose of the Study:

  • To develop a low-cost, IoT-based monitoring system for predictive maintenance of small rotating machinery.
  • To assess the effectiveness of vibration and acoustic signal analysis combined with machine learning for fault detection.

Main Methods:

  • Utilized an ESP32 microcontroller with MEMS accelerometer and microphone sensors.
  • Collected and processed vibration and acoustic signals using Root Mean Square (RMS) and Fast Fourier Transform (FFT) techniques.
  • Applied machine learning algorithms (anomaly detection, classification) to identify operational deviations.

Main Results:

  • A working prototype successfully identified abnormal states under fault conditions (imbalance, wear).
  • The system demonstrated over ~73% accuracy in detecting faults through time and frequency domain signal analysis.
  • The proposed solution proved cost-effective and simple to implement.

Conclusions:

  • The developed IoT system offers a scalable and accessible approach to predictive maintenance for small machinery.
  • Embedded systems and basic signal analysis hold significant potential for industrial and educational applications.
  • This research validates the feasibility of low-cost sensor networks for enhancing machinery reliability.