Shibin Wang

TL;DR: The authors introduce an iterative algorithm, called matching demodulation transform (MDT), to generate a time-frequency (TF) representation with satisfactory energy concentration, and the MDT-based synchrosqueezing algorithm is described to further enhance the concentration and reduce the diffusion of the curved IF profile in the TF representation of original syn chrosquEEzing transform.

TL;DR: A nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms.

TL;DR: A comprehensive evaluation of DL-based intelligent diagnosis models with two data split strategies, five input formats, three normalization methods, and four augmentation methods is performed, and a unified code framework for comparing and testing models fairly and quickly is released.

TL;DR: Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection in this paper, and the proposed method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period.

TL;DR: Zhao et al. as mentioned in this paper constructed a taxonomy and performed a comprehensive review of unsupervised deep transfer learning (UDTL)-based intelligent fault diagnosis (IFD) according to different tasks.