Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Fault Prognostic System for the Logistics Rotational Equipment

물류 회전설비 고장예지 시스템

  • Received : 2023.05.31
  • Accepted : 2023.06.22
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

Keywords

Acknowledgement

This work was supported by the Innopolis Foundation grant funded by the Ministry of Science and ICT (No. 1711175676, Development of a Fault Prognostic System for Power Plant Rotational Equipment based on AI Technologies)

References

  1. Banks, M., 8 Causes of Lost Productivity in Manufacturing and How Managers Can Address Them in 2022, Manufactoring Tomorrow, March 14, 2022. https://www.manufacturingtomorrow.com/story/2022/03/8-causes-of-lost-productivity-in-manufacturing-and-how-managers-can-address-them-in-2022/18384/.
  2. Berry, J., Predictive Maintenance and Vibration Signature Analysis I, first ed., Entek IRD International, 1993, pp. 2-6-6-64.
  3. da Silva, R.M. Frederico, G.F., and Garza-Reyes, J.A., Logistics Service Providers and Industry 4.0: A Systemati c Literature Review, Logistics, 2023, 7, 11. https://doi.org/10.3390/logistics7010011.
  4. Global Market Trend Report - Prognositics and Health Management Market, Innopolis Report, 2021. pp. 1-17.
  5. Jo, A.H., CJ Daehantongun, Daejeonteomineol Gadongjungdane Yeonmal Taekbae Bisang, Newspim, November 14, 2018. https://www.newspim.com/news/view/20181113000806.
  6. Jung, H. and Kim, J.A., Machine Learning Approach for Mechanical Motor Fault Diagnosis, Journal of Korean Society of Industrial and System Engineering, 2017, pp. 57-64.
  7. Kim, S., Berdibayev, Y., Park, J., Jung, J., Lee, S., Kim, K., Jo, H., Ahn, C., and Kim, J., A Fault Diagnostic Model for Electric Rotating Machines, Proceedings of the 10th International Conference on Big Data Applications and Services, 2022, Jeju Island, Korea, pp. 51-58.
  8. Saruhan, H., Saridemir, S., Qicek, A., and Uygur, I., Vibration analysis of rolling element bearings defects. Journal of Applied Research and Technology, 2014, Vol. 12, No. 3, pp. 384-395. https://doi.org/10.1016/S1665-6423(14)71620-7
  9. Sim, J., Kim, S., Park, H.J., and Choi, J.-H. A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings, Appl. Sci., 2020, Vol. 10, p. 5639. https://doi.org/10.3390/app10165639.
  10. Wilson, S.J., Data Representation for Time Series Data Mining: Time Domain Approaches, Wiley Interdisciplinary Reviews: Computational Statistics, 2017, Vol. 9, No. 1, pp. e1392:1-6.
  11. Yu, G.S. and Moon, Y.M., Development Trend of Smart Factory Facility Diagnosis and Predictive Maintenance Technology, The Journal of The Korean Institute of Communication Sciences, 2020, Vol. 37, No. 7, pp. 36-42.