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회전기기에 대한 데이터 주도 고장진단 및 예측 모형 개발

Title
회전기기에 대한 데이터 주도 고장진단 및 예측 모형 개발
Other Titles
Developing the Fault Diagnostics and Prognostics Model of a Rotating Machinery
Author
안선응
Keywords
Fault Diagnostics; Condition based Maintenace(CBM); Marckov Chain Monte Carlo (MCMC); Prognostics and Health Management(PHM); Rotating Machinery
Issue Date
2020-02
Publisher
한국경영과학회
Citation
한국경영과학회지, v. 45, no. 1, page. 25-38
Abstract
With the advent of the fourth industrial revolution in recent years, engineering systems require prognostics and health management techniques that can accurately diagnose and prognose the state of the system. In this study, we developed a state-of-the-art fault diagnostics model using real-time vibration signals in the engineering systems. We established a degradation model that follows the Weibull hazard function and performed Bayesian estimation based on Markov chain Monte Carlo simulation to develop a fault diagnostics model. From the fault diagnostics model, we developed a data-driven fault prognostics model by monitoring the condition of the system continuously. In the experiment, this model is applied in the actual Intelligent Maintenance System bearing data provided by the University of Cincinnati. Compared with the previous methods, the results showed better performance.
URI
https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002563624https://repository.hanyang.ac.kr/handle/20.500.11754/163136
ISSN
1225-1119
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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