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Related Experiment Video

Updated: Sep 22, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge

Özgür Gültekin1,2, Eyup Cinar2,3, Kemal Özkan2,3

  • 1Department of Informatics, Eskisehir Osmangazi University, Eskisehir 26040, Turkey.

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Summary

This study introduces an edge AI and FIWARE system for early fault detection in industrial equipment. The system significantly reduces bandwidth and data transfer times for real-time condition monitoring.

Keywords:
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Area of Science:

  • Industrial IoT and Smart Manufacturing
  • Artificial Intelligence in Predictive Maintenance
  • Real-time Data Processing and Analytics

Background:

  • Unplanned industrial equipment downtime disrupts production cycles, capacity, and customer trust.
  • Smart manufacturing necessitates advanced fault detection and classification systems for continuous operation.
  • Existing systems often require optimization for real-time performance and efficient data handling.

Purpose of the Study:

  • To propose a generic, real-time fault diagnosis and condition monitoring system.
  • To integrate edge artificial intelligence (edge AI) with the FIWARE open-source middleware.
  • To demonstrate the system's effectiveness for autonomous transfer vehicle (ATV) monitoring in a smart factory context.

Main Methods:

  • Development of a flexible system architecture with expandable interfaces for diverse devices.
  • Implementation of a deep learning-based fault diagnosis method within an edge AI unit.
  • Utilization of a data pipeline for transferring diagnostic results to data storage.

Main Results:

  • The edge AI solution achieved significant real-time performance for ATV fault diagnosis.
  • Network bandwidth requirements were reduced by 43 times.
  • Total elapsed data transfer time was reduced by 37 times.

Conclusions:

  • The proposed system enables effective real-time monitoring of ATV fault conditions.
  • The architecture is scalable for monitoring fleets of equipment in manufacturing facilities.
  • This approach enhances industrial system reliability and operational efficiency through early fault detection.