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이도환(Do Hwan Lee),이선기(Sun Ki Lee),정래혁(Rae Hyuk Jung),조민호(Min Ho Cho) 한국소음진동공학회 2010 한국소음진동공학회 학술대회논문집 Vol.2010 No.10
This paper presents a wear diagnosis method for centrifugal impellers by using an accelerometer. The features are calculated from raw and wavelet transformed signals with several statistical methods applied in time or frequency domains. From the effectiveness coefficient test, it is shown that 7th level of wavelet transformed signal is suitable for wear classification problems. A neural network with 5 feature sets is applied to diagnose the wear magnitude of pump impellers. The verification result reveals that high accuracy for the wear diagnosis of impellers can be obtained by using wavelet features transformed from acceleration signals.
이도환(Do Hwan Lee),김양석(Yang Seok Kim) 대한기계학회 2012 대한기계학회 춘추학술대회 Vol.2012 No.11
This paper presents a prognostic technique for the Remaining Useful Life (RUL) of a ball bearing. A state-space model based on Paris law is applied to estimate the damage progression. The hidden damage state and the time to failure are computed by using noisy measurements obtained from a accelerometer. In order to identify the model parameters, a series of run-to-failure tests for ball bearings has been carried out. A Particle Filter approach is used to predict the demage progression rate and update the degradation state based on the most recent measurement. The future state and the RUL are predicted by the assumption that the previous state is not different from the unobserved measurement. The developed method was validated within the predicted uncertainty by comparing the prognostic results and test data.
이도환(Do Hwan Lee),김양석(Yang Seok Kim) 대한기계학회 2013 大韓機械學會論文集A Vol.37 No.11
볼 베어링의 손상 상태를 예측하기 위한 방법을 본 논문에서 제시하였다. 손상 진전율을 추정하기 위해 확률적 베어링 피로 결함 진전 모델을 적용하고 잡음이 포함된 가속도 신호의 RMS 데이터를 이용하여 손상 상태와 고장 시간을 계산하였다. 확률적 결함 진전 모델의 파라미터는 볼 베어링에 대한 일련의 Run-to-Failure 시험을 수행하여 결정하였다. 가속도 RMS값으로부터 손상 진전율과 손상 상태를 추정하기 위해 규칙화된 파티클 필터 추정 방법을 적용하였다. 미래 시점에서의 손상 상태는 최근 측정된 데이터와 직전에 추정된 상태값을 이용하여 예측하였다. 예측된 손상 상태와 시험 데이터와 비교하여 개발된 방법의 적절성을 확인하였다. This study presents a prognostic technique for the damage state of a ball bearing. A stochastic bearing fatigue defect-propagation model is applied to estimate the damage progression rate. The damage state and the time to failure are computed by using RMS data from noisy acceleration signals. The parameters of the stochastic defect-propagation model are identified by conducting a series of run-to-failure tests for ball bearings. A regularized particle filter is applied to predict the damage progression rate and update the degradation state based on the acceleration RMS data. The future damage state is predicted based on the most recently measured data and the previously predicted damage state. The developed method was validated by comparing the prognostic results and the test data.