As many systems become automated, system maintenance is becoming more critical. It is important always to monitor the system condition to maintain the system more efficiently and stably. In this paper, we propose a probability-based algorithm that analyzes time-series data of a complex system. We evaluate various system conditions with high accuracy by analyzing critical data among time-series data with GMM-based probability.
|Title of host publication||ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2021|
|Event||24th International Conference on Electrical Machines and Systems, ICEMS 2021 - Gyeongju, Korea, Republic of|
Duration: 2021 Oct 31 → 2021 Nov 3
|Name||ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems|
|Conference||24th International Conference on Electrical Machines and Systems, ICEMS 2021|
|Country/Territory||Korea, Republic of|
|Period||21/10/31 → 21/11/3|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2020R1A2B5B01002395).
© 2021 KIEE & EMECS.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Mechanical Engineering
- Safety, Risk, Reliability and Quality