머신러닝을 이용한 종이용기 성형기의 캠 마모 고장 진단 알고리즘개발Development of Diagnosis Algorithm for Cam Wear of Paper Container Using Machine Learning
- Other Titles
- Development of Diagnosis Algorithm for Cam Wear of Paper Container Using Machine Learning
- Authors
- 주백석; 김설하; 장재호
- Issue Date
- 2019
- Publisher
- 한국정밀공학회
- Keywords
- Cam wear (캠 마모); Machine learning (기계학습); Paper cup forming machine (종이용기 성형기); Failure diagnosis (고장 진단); K-Nearest neighbor classifier (K 근접 이웃 분류기)
- Citation
- 한국정밀공학회지, v.36, no.10, pp.953 - 959
- Journal Title
- 한국정밀공학회지
- Volume
- 36
- Number
- 10
- Start Page
- 953
- End Page
- 959
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/226
- DOI
- 10.7736/KSPE.2019.36.10.953
- ISSN
- 1225-9071
- Abstract
- Recently, improvement of productivity of the paper cup forming machine has being conducted by increasing manufacturing speed. However, rapid manufacturing speed imposes high load on cams and cam followers. It accelerates wear and cracking, and increases paper cup failure. In this study, a failure diagnosis algorithm was suggested using vibration data measured from cam driving parts. Among various paper cup forming processes, a test bed imitating the bottom paper attaching process was manufactured. Accelerometers were installed on the test bed to collect data. To diagnose failure from measured data, the K-NN (K-Nearest Neighbor) classifier was used. To find a decision boundary between normal and abnormal state, learning data were collected from normal and abnormal state, and normal and abnormal cams. A few representative features such as mean and variance were selected and transformed to the relevant form for the classifier. Classification experiments were performed with the developed classifier and data gathered from the test bed. According to assigned K values, a successful classification result was obtained which means appropriate failure recognition.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - School of Mechanical System Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.