메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
Andrian Julianto (Pertamina) Aria Bisma Wahyutama (Changwon National University) Mintae Hwang (Changwon National University)
저널정보
한국정보통신학회 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING 2023 INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING Vo.14 No.1
발행연도
2023.1
수록면
6 - 9 (4page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
This paper introduces the preventive maintenance method on various oiling machines as an early warning system and the design of a digital dashboard for delivering and reporting data as a replacement for traditional and cumbersome data reporting systems. The objective of this paper is to introduce an early warning monitoring system and design of digital dashboard that can be applied to delivering and reporting monitoring data of fast-moving machines such as rotating equipment. A preventive maintenance methodology called condition monitoring is conducted to check the status of each machine on multiple oiling online rotating equipment by measuring various parameters such as thermal and vibration. The vibration data will be converted into waveform and spectrum to decide which factor that could cause damage. Furthermore, based on the obtained data and according to ISO10816-3, an expert panel will assess and determine the machine's condition from three thresholds, which are normal, warning, and unacceptable. Once the necessary data has been gathered, a digital dashboard provided by Microsoft Power Business Intelligence (BI) is then designed to deliver the data which will be used to further analyze and perform decision-making that prevents a certain machine from completely breaking down and requiring an overhaul.

목차

Abstract
Ⅰ. INTRODUCTION
Ⅱ. SYSTEM FLOWCHART AND METHODS
Ⅲ. DIGITAL DASHBOARD DESIGN
Ⅳ. CONCLUSIONS AND FUTURE STUDIES
REFERENCES

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0