특징에 따른 기어박스 결함의 진동신호 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 gear damage effect 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 GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature classification is evaluated by faults on the gearbox. the research compared the possibility of classification through the feature of the existing rotor failure classification and the new feature applicable to 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 98%, which verifies the performance for gearbox fault diagnosis.