Journal ArticleDOI
Blind deconvolution based on cyclostationarity maximization and its application to fault identification
TLDR
A novel blind deconvolution method based on the generalized Rayleigh quotient and solved by means of an iterative eigenvalue decomposition algorithm reveals superior capability to recover impulsive cyclostationary sources with respect to other blind deconVolution methods, even in the presence of impulsive noise or under non-constant speed.About:
This article is published in Journal of Sound and Vibration.The article was published on 2018-10-13. It has received 135 citations till now. The article focuses on the topics: Blind deconvolution & Deconvolution.read more
Citations
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Journal ArticleDOI
Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution
TL;DR: An adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed, aiming at the determination of cyclic frequency set estimation method based on autocorrelation function of morphological envelope and the validity of the method is verified.
Journal ArticleDOI
Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring
TL;DR: The proposed approach is a simple yet effective way of tracking faults with a cyclostationary signature and key in the iterative optimization procedure is the usage of the Rayleigh quotient to update the filter coefficients.
Journal ArticleDOI
Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution
TL;DR: In this article , an adaptive maximum cyclostationarity blind deconvolution (ACYCBD) method was proposed for fault detection, which can recover periodic impulses from mixed fault signals convoluted by noise and periodic impulses.
Journal ArticleDOI
A review on the application of blind deconvolution in machinery fault diagnosis
Yonghao Miao,Yonghao Miao,Archana Rajmane,Boyao Zhang,Jing Lin,Ming Zhao,Hanyang Liu,Zongyang Liu,Hao Li +8 more
TL;DR: This paper provides a comprehensive review of blind deconvolution methods from history to state-of-the-art methods and finally to research prospects, as well as provides a survey and summarize the current progress of BDMs applied in machinery fault diagnosis.
Journal ArticleDOI
Practical framework of Gini index in the application of machinery fault feature extraction
TL;DR: A new GI evaluation frame is built, including the definition of new indexes based on GI, and enhancing signal processing methods via GI, such as spectrum kurtosis, decomposition methods, and multi-objective optimization algorithms are designed.
References
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Pattern Recognition and Machine Learning (Information Science and Statistics)
TL;DR: Looking for competent reading resources?
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Independent Component Analysis
TL;DR: Independent component analysis as mentioned in this paper is a statistical generative model based on sparse coding, which is basically a proper probabilistic formulation of the ideas underpinning sparse coding and can be interpreted as providing a Bayesian prior.
Reference EntryDOI
Independent Component Analysis
Hannu Oja,Klaus Nordhausen +1 more
TL;DR: A statistical generative model called independent component analysis is discussed, which shows how sparse coding can be interpreted as providing a Bayesian prior, and answers some questions which were not properly answered in the sparse coding framework.