PCA-SVM 기반의 SMPS 고장예지에 관한 연구

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.