Cheng Junsheng

TL;DR: The analysis results from roller bearing signals with inner-race and out-race faults show that the diagnosis approach based on neural network by using EMD to extract the energy of different frequency bands as features can identify roller bearing fault patterns accurately and effectively and is superior to that based on wavelet packet decomposition and reconstruction.

TL;DR: In this article, the authors proposed a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings.

TL;DR: In this article, an energy difference tracking method is proposed to define the intrinsic mode function (IMF) in the EMD method, based on the integrity and orthogonality of the EMF.

TL;DR: In this article, an energy operator demodulation approach based on EMD (Empirical Mode Decomposition) is proposed to extract the instantaneous frequencies and amplitudes of the multi-component amplitude-modulated and frequency modulated (AM-FM) signals.

TL;DR: Enhanced deep transfer auto-encoder is proposed for fault diagnosis of bearing installed in different machines using scaled exponential linear unit and target training samples with limited labeled information to improve the quality of the mapped vibration data collected from bearing.