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Erschienen in:
2015 | OriginalPaper | Buchkapitel
67. Signal-Processing Technology for Rotating Machinery Fault Signal Diagnosis
verfasst von : Byung Hyun Ahn, Yong Hwi Kim, Jong Myeong Lee, Jeong Min Ha, Byeong Keun Choi
Erschienen in: Progress in Clean Energy, Volume 1
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Abstract
The acoustic emission (AE) technique is widely applied to develop early fault detection systems, on which the problem of a signal-processing method for an AE signal is mainly focused. In the signal-processing method, envelope analysis is a useful method to evaluate the bearing problems and the wavelet transform is a powerful method to detect faults occurring on rotating machinery. However, an exact method for the AE signal has not been developed yet. Therefore, in this chapter two methods are given: Hilbert transform and discrete wavelet transform (IEA), and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET and 0.01–1.0 for the RBF kernel function of SVR; the proposed algorithm achieved 94 % classification accuracy with the parameter of the RBF 0.08, 12 feature selection.