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This article explores the monitoring of machine conditions with a focus on anomaly detection using IO-Link technology. The primary goal is to monitor the operational status of machinery and predict future performance based on the analysis of vibrations and other relevant parameters. The article provides an in-depth discussion on data collection methods, emphasizing the role of IO-Link in enabling efficient and precise data acquisition. Various vibration analysis techniques, including Root Mean Square (RMS) and Fast Fourier Transform (FFT), are examined to evaluate their effectiveness in identifying potential issues. Additionally, the research outlines an experimental setup involving an electric motor and multiple sensors to simulate real-world conditions and assess the applicability of these methods. By integrating advanced monitoring technologies and analysis techniques, this study aims to enhance predictive maintenance strategies, improve machine reliability, and minimize downtime. The findings highlight the potential of IO-Link technology as a robust tool for industrial applications, offering valuable insights into optimizing machine performance and operational efficiency.

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Detection of Anomalies in Machine Operation Based on Use of IO-Link Technology and Vibration Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Marek Vagas,
  • Alena Galajdova,
  • Robert Rakay,
  • Radovan Holubek

摘要

This article explores the monitoring of machine conditions with a focus on anomaly detection using IO-Link technology. The primary goal is to monitor the operational status of machinery and predict future performance based on the analysis of vibrations and other relevant parameters. The article provides an in-depth discussion on data collection methods, emphasizing the role of IO-Link in enabling efficient and precise data acquisition. Various vibration analysis techniques, including Root Mean Square (RMS) and Fast Fourier Transform (FFT), are examined to evaluate their effectiveness in identifying potential issues. Additionally, the research outlines an experimental setup involving an electric motor and multiple sensors to simulate real-world conditions and assess the applicability of these methods. By integrating advanced monitoring technologies and analysis techniques, this study aims to enhance predictive maintenance strategies, improve machine reliability, and minimize downtime. The findings highlight the potential of IO-Link technology as a robust tool for industrial applications, offering valuable insights into optimizing machine performance and operational efficiency.