Yu Yang

TL;DR: In this article, the authors proposed a method for the fault diagnosis of roller bearings based on EMD and Hilbert spectrum analysis of wavelet coefficients of high scales, which can obtain the local Hilbert marginal spectrum from which the faults in a bearing can be diagnosed and fault patterns can be identified.

TL;DR: In this article, a method of fault feature extraction based on intrinsic mode function (IMF) envelope spectrum is proposed to overcome the limitations of conventional envelope analysis method by utilizing the proposed feature extraction method, the disadvantages of conventional analysis method such as the chosen of central frequency of filter with experience in advance, looking for spectral line of fault characteristic frequencies in envelope spectrum and so on could be overcome.

TL;DR: A new rolling bearing fault diagnosis approach based on LCD and FuzzyEn based on local characteristic-scale decomposition is proposed and results show that the proposed method performs effectively for the rolling Bearing fault diagnosis.

TL;DR: A novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples and transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach.

TL;DR: An improved CNN named multi-scale cascade convolutional neural network (MC-CNN) is proposed for the classification information enhancement of input and is verified by analyzing the application of MC-CNN in bearing fault diagnosis under nonstationary working conditions.