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최병근(Byeong-Keun Choi),안병현(Byung-Hyun Ahn),김용휘(Yong-Hwi Kim),이종명(Jong-Myeong Lee),이정훈(Jeong-Hoon Lee) 한국소음진동공학회 2013 한국소음진동공학회 학술대회논문집 Vol.2013 No.10
Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.
안병현(Byung-Hyun Ahn),김용휘(Yong-Hwi Kim),이종명(Jong-Myeong Lee),이정훈(Jeong-Hoon Lee),최병근(Byeong-Keun Choi) 한국소음진동공학회 2014 한국소음진동공학회 논문집 Vol.24 No.7
Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.