Real-Time Anomaly Detection in Industry 4.0 Using Asset Administration Shell

Kaya F., Ünal A., Albayrak Ö., Ünal P., Kırcı P.

21st International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2025, İstanbul, Turkey, 11 - 13 August 2025, vol.16066 LNCS, pp.114-128, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 16066 LNCS
  • Doi Number: 10.1007/978-3-032-02060-4_8
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.114-128
  • Keywords: Anomaly Detection, Asset Administration Shell, Industry 4.0, Mahalanobis Distance, Median Absolute Deviation
  • Bursa Uludag University Affiliated: Yes

Abstract

The rapid advancement of Industry 4.0 necessitates robust and interoperable digital twin technologies supported by structured semantic frameworks such as the Asset Administration Shell (AAS). This paper systematically explores aspects of Industry 4.0 implementations, including semantic interoperability via AAS, standardized data formats such as AASX, time-series data management, and middleware solutions. Emphasis is placed on unsupervised anomaly detection techniques—Median Absolute Deviation (MAD) and Mahalanobis Distance—within industrial streaming data environments. Utilizing a case study, sensor data were analyzed through a developed Eclipse BaSyx plugin integrated with InfluxDB and MQTT, demonstrating effective real-time anomaly detection. The findings underscore the importance of adaptable and standardized semantic integration for achieving optimized operational efficiency in Industry 4.0.