PCA-SVM 기반의 SMPS 고장예지에 관한 연구Fault Prognostics of a SMPS based on PCA-SVM
- Other Titles
- Fault Prognostics of a SMPS based on PCA-SVM
- Authors
- 유연수; 김동현; 김설; 허장욱
- Issue Date
- Sep-2020
- Publisher
- 한국기계가공학회
- Keywords
- Failure Prognostic(고장예지); Machine Learning(기계학습); Switching Mode Power Supply(스위칭모드 전원공급장치); Prognostics and Health Management(고장예지 및 건전성 관리)
- Citation
- 한국기계가공학회지, v.19, no.9, pp.47 - 52
- Journal Title
- 한국기계가공학회지
- Volume
- 19
- Number
- 9
- Start Page
- 47
- End Page
- 52
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/19069
- DOI
- 10.14775/ksmpe.2020.19.09.047
- ISSN
- 1598-6721
- Abstract
- With the 4th industrial revolution, condition monitoring using machine learning techniques has becomepopular among researchers. An overload due to complex operations causes several irregularities in MOSFETs.
This study investigated the acquired voltage to analyze the overcurrent effects on MOSFETs using a failuremode effect analysis (FMEA). The results indicated that the voltage pattern changes greatly when the currentis beyond the threshold value. Several features were extracted from the collected voltage signals that indicatethe health state of a switched-mode power supply (SMPS). Then, the data were reduced to a smaller samplespace by using a principal component analysis (PCA). A robust machine learning algorithm, the supportvector machine (SVM), was used to classify different health states of an SMPS, and the classification resultsare presented for different parameters. An SVM approach assisted by a PCA algorithm provides a strong faultdiagnosis framework for an SMPS.
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Collections - School of Mechanical System Engineering > 1. Journal Articles
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