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http://dx.doi.org/10.5050/KSNVE.2014.24.7.555

Signal Processing Technology for Rotating Machinery Fault Signal Diagnosis  

Ahn, Byung-Hyun (Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea)
Kim, Yong-Hwi (Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea)
Lee, Jong-Myeong (Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea)
Lee, Jeong-Hoon (Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea)
Choi, Byeong-Keun (Department of Energy and Mechanical Engineering at Gyeongsang National University in Korea)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.24, no.7, 2014 , pp. 555-561 More about this Journal
Abstract
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.
Keywords
Acoustic Emission; Signal Processing; Hilbert Transform; Fault Classification; Feature Selection;
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