AN IOT DATA ANOMALY RESPONSE MODEL FOR SMART FACTORY PERFORMANCE MEASUREMENT

Authors

  • Gyusun Hwang Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea
  • Sunga Kang Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea
  • Abdallah Jamal Dweekat Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea
  • Jinwoo Park Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea
  • Tai-Woo Chang Department of Industrial and Management Engineering, Kyonggi University, Suwon, Republic of Korea

DOI:

https://doi.org/10.23055/ijietap.2018.25.5.3731

Keywords:

Internet of Things, Performance measurement system, Overall Equipment Effectiveness, Fault tolerance, Data anomaly analysis

Abstract

The recent rapid proliferation of Internet of Things increases the possibility of IoT failure due to complexity of the network and large data volumes. The demand for clear acquisition of data as an important source of performance measurement presents manufacturing companies with a major challenge. To cope with IoT fault problem, we developed the performance measurement and IoT anomaly response model. Development comprises three steps: (1) configuration of the IoT-based performance measurement process using an Overall Equipment Effectiveness Key Performance Indictor and IoT anomaly response model architecture; (2) implementation of an IoT fault case classification and data anomaly detection and mitigation algorithm, using K-means and statistical inference methods; and (3) validation of the proposed algorithm through experimental simulation. The experimental simulation results show that the proposed algorithm has a positive impact on IoT data anomaly detection and mitigation, thus enabling a response to the IoT fault problem.

Published

2019-01-03

How to Cite

Hwang, G., Kang, S., Dweekat, A. J., Park, J., & Chang, T.-W. (2019). AN IOT DATA ANOMALY RESPONSE MODEL FOR SMART FACTORY PERFORMANCE MEASUREMENT. International Journal of Industrial Engineering: Theory, Applications, and Practice, 25(5). https://doi.org/10.23055/ijietap.2018.25.5.3731