베어링 결함이 있는 회전기계의 상태 진단을 위한 은닉 마르코프 모델의 적용

Abstract

In recent research, pattern recognition method has been widely used by many researchers for fault diagnoses of mechanical systems. Also it determines the soundness of a mechanical system by detecting variations in the systems's vibration characteristics. Hidden Markov model (HMM) has recently been used as pattern recognition methods in various fields. In this study, a HMM method for the fault diagnosis of a rotating machine with bearing fault is introduced. The existence, location, and quantity of bearing fault are identified. Also Fast Fourier Transform (FFT) is employed to extract feature vector.