기어박스 결함 유형에 따른 고장진단을 위한 특징 분석

Abstract

In the development of a fault diagnosis and condition monitoring in the gearbox, the research is a quantitative analysis and a test of the effect of gear damage on the vibration of the gearbox. The lab-scale gearbox test device that builds the several types of fault such as gear tooth breakage, misalignment and looseness occurred by gearbox fault simulator. This paper presents feature analysis through the PCA (principal component analysis), GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature classification is evaluated by faults on the gearbox. In addition, the trend of selected features of combined fault indicates clustering at the same point on three dimensions. Therefore, the results of feature-based analysis considering gearbox faults are about 99%, which verifies the performance for gearbox fault diagnosis.