331 0

웨이블릿 스펙트럼을 이용한스마트 팩토리 설비의 이상감지 및 진단

Title
웨이블릿 스펙트럼을 이용한스마트 팩토리 설비의 이상감지 및 진단
Other Titles
Fault Detection and Diagnosis of Smart Factory Equipments Using Wavelet Spectrum
Author
배석주
Keywords
Feature Extraction; Smart Factory; Signal Processing; Support Vector Machine; Wavelet Transform
Issue Date
2019-03
Publisher
한국신뢰성학회
Citation
신뢰성 응용연구, v. 19, NO 1, Page. 22-30
Abstract
Purpose: Condition-based maintenance (CBM) is widely used to decrease the risk of equipment failures. A signal data indicating the health status of equipments is continuously measured in CBM. This article proposes a fault detection and diagnosis approach for smart factory equipments based on the signal processing and feature extraction techniques using a support vector machine (SVM). Methods: We propose a discrete wavelet transform (DWT) as one of signal processing methods. After processing the signal data, we derive the representative energy spectrum through various measures such as mean, median, variance, and interquartile range (IQR). Finally, the SVM is used to classify two classes based on Gaussian radial basis function (RBF) kernel. Results: we applied the proposed method to signal data collected from the equipment. We compared the classification accuracy of the SVM. At window length of , the wavelet spectrum through the variance measure provides the best classification accuracy for the signal data of the equipment. Conclusion: In this article, fault detection and diagnosis methods for smart factory equipments are proposed.
URI
http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07994423&language=ko_KRhttps://repository.hanyang.ac.kr/handle/20.500.11754/139438
ISSN
1738-9895
DOI
10.33162/JAR.2019.03.19.1.22
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE