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Cong yi Zhang(장총위),Sui-jin Kim(김수진),Sung-Ho Kim(김성호) 한국지능시스템학회 2009 한국지능시스템학회 학술발표 논문집 Vol.19 No.1
In this paper, a fault diagnosis system for rotating machine using wavelet packet transform (WPT) and artificial neural network (ANN) is described. In most fault diagnosis for rotating machines, WPT is a well-known signal processing technique for previous work used for speech recognition. In previous work, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the wavelets are used as mother wavelets to build and perform the proposed WPT technique. In the classification, an Elman neural network is utilized.