인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2020.10
- 수록면
- 443 - 446 (4page)
이용수
초록· 키워드
Maintenance of an industrial electric motor is very important. The most commonly used algorithm for deep learning motor diagnosis using deep learning is CNN, which is one of the representative supervised learning algorithms. However, the failure diagnosis algorithm made with the CNN algorithm is vulnerable to this data. For this reason, an algorithm that complements this has been proposed, and that is to use the RNN and K-means algorithms. The method using RNN has a cyclic neural network structure, so it can grasp the similarity of data. K-means also uses the Euclidean distance method to grasp the similarity between data and classify the data using it. Due to the characteristics of these two algorithms, even if a disturbance is an input, if the similarity of data is high, it is determined as similar data. In this paper, two algorithms were used to perform fault diagnosis and two experiments were conducted to understand the differences and characteristics of the two algorithms. As a result of experiment 1 classifying only normal failures, experiment 2 experimented by increasing the number of failures to be classified. In the case of RNN, the results of experiments 1 and 2 showed similar accuracy. However, in the case of the algorithm using K-means, the accuracy decreased as the number of classifications increased.
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목차
- Abstract
- 1. INTRODUCTION
- 2. PROPOSED FAULT DIAGNOSIS ALGORITHM
- 3. EXPERIMENT
- 4. CONCLUSION
- REFERENCES
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2020-003-001570226
