Treffer: 양식장 펌프 모터 전류 데이터를 이용한 머신러닝 기반 이상 감지 알고리즘에 관한 연구.

Title:
양식장 펌프 모터 전류 데이터를 이용한 머신러닝 기반 이상 감지 알고리즘에 관한 연구.
Alternate Title:
A study on machine learning-based anomaly detection algorithm using current data of fish-farm pump motor.
Authors:
박 세 용1,2 parksaeyong98@gmail.com, 장 태 욱3,4 michael.chang@q-sol.co.kr, 임 태 호5,6 taehoim@hoseo.edu
Source:
Journal of Internet Computing & Services. Apr2023, Vol. 24 Issue 2, p37-45. 9p.
Database:
Business Source Premier

Weitere Informationen

In line with the 4th Industrial Revolution, facility maintenance technologies for building smart factories are receiving attention and are being advanced. In addition, technology is being applied to smart farms and smart fisheries following smart factories. Among them, in the case of a recirculating aquaculture system, there is a motor pump that circulates water for a stable quality environment in the tank. Motor pump maintenance activities for recirculating aquaculture system are carried out based on preventive maintenance and data obtained from vibration sensor. Preventive maintenance cannot cope with abnormalities that occur before prior planning, and vibration sensors are affected by the external environment. This paper proposes an anomaly detection algorithm that utilizes ADTK, a Python open source, for motor pump anomaly detection based on data collected through current sensors that are less affected by the external environment than noise, temperature and vibration sensors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Internet Computing & Services is the property of Korean Society for Internet Information and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)