Signal-processing technology for rotating machinery fault signal diagnosis

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. ? Springer International Publishing Switzerland 2015.