Journal ArticleDOI
Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances
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TLDR
A class of methods in TFA, parameterised TFA is focused on, summarizing its latest research progress and related engineering applications, so as to provide reference and guidance for researchers applying parametric TFA in different fields.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2019-03-15. It has received 130 citations till now.read more
Citations
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Journal ArticleDOI
High-accuracy fault feature extraction for rolling bearings under time-varying speed conditions using an iterative envelope-tracking filter
TL;DR: Besides estimating the signal envelope, the IETF incorporates an iterative frequency-refinement procedure to accurately estimate the FCF of the faulty bearing, and a high-resolution TFR based on the estimated signal envelopes and frequencies is constructed to clearly reveal bearing fault features.
Journal ArticleDOI
Summary of over Fifty Years with Brain-Computer Interfaces-A Review.
Aleksandra Kawala-Sterniuk,Natalia Browarska,Amir F. Al-Bakri,Mariusz Pelc,Mariusz Pelc,Jarosław Zygarlicki,Michaela Sidikova,Radek Martinek,Edward Jacek Gorzelanczyk +8 more
TL;DR: In this paper, the authors present the most relevant aspects of the BCI and all the milestones that have been made over nearly 50-year history of this research domain and highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many.
Journal ArticleDOI
A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis
TL;DR: A weighted multi-scale dictionary learning model (WMSDL) is proposed in this paper which integrates the multi- scale transform and fault information into a unified dictionary learning models and it successfully overcomes four disadvantages of traditional dictionary learning algorithms.
Journal ArticleDOI
Synthesis versus analysis priors via generalized minimax-concave penalty for sparsity-assisted machinery fault diagnosis
Shibin Wang,Shibin Wang,Ivan Selesnick,Gaigai Cai,Gaigai Cai,Baoqing Ding,Baoqing Ding,Xuefeng Chen +7 more
TL;DR: In this paper, a generalized minimax-concave (GMC) penalty was introduced for sparse regularization problems to improve the performance of signal denoising or signal decomposition for the purpose of machinery fault diagnosis.
Journal ArticleDOI
A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images
TL;DR: Wang et al. as mentioned in this paper employed Bayesian optimization (BO) for adaptive HP learning, and an improved convolutional neural network (CNN) was established for fault feature extraction and classification in a hydraulic piston pump.
References
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Proceedings ArticleDOI
Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition
TL;DR: A modification to the matching pursuit algorithm of Mallat and Zhang (1992) that maintains full backward orthogonality of the residual at every step and thereby leads to improved convergence is proposed.
Journal ArticleDOI
Atomic Decomposition by Basis Pursuit
TL;DR: This work gives examples exhibiting several advantages over MOF, MP, and BOB, including better sparsity and superresolution, and obtains reasonable success with a primal-dual logarithmic barrier method and conjugate-gradient solver.
Journal ArticleDOI
Time-frequency distributions-a review
TL;DR: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented with emphasis on the diversity of concepts and motivations that have gone into the formation of the field.
Journal ArticleDOI
Localization of the complex spectrum: the S transform
TL;DR: The S transform is shown to have some desirable characteristics that are absent in the continuous wavelet transform, and provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.
Journal Article
Localisation of the complex spectrum : The S transform
TL;DR: The S transform as discussed by the authors is an extension to the ideas of the Gabor transform and the Wavelet transform, based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms.