Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform

Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform

In order to extract fault impulse feature of large-scale rotating machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined multiresolution generalized S transform is proposed in this paper. In this method, multiresolution generalized S transfo...

Full description

Bibliographic Details
Main Authors:	Baokang Yan, Bin Wang, Fengxing Zhou, Weigang Li, Bo Xu
Format:	Article
Language:	English
Published:	SAGE Publishing 2019-06-01
Series:	Journal of Low Frequency Noise, Vibration and Active Control
Online Access:	https://doi.org/10.1177/1461348418825406

Similar Items

Rotating machinery fault diagnosis based on improver resonance sparse decomposition
by: Wei Yang, et al.
Published: (2019-10-01)

Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery
by: Yumei Qi, et al.
Published: (2017-01-01)

Transform-domain sparse representation based classification for machinery vibration signals
by: Yu Fajun, et al.
Published: (2018-03-01)

Sparse Representation Classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis
by: Yun Kong, et al.
Published: (2021-01-01)

Classification of machinery vibration signals based on group sparse representation
by: Fajun Yu, et al.
Published: (2016-05-01)

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering
by: Weiwei Qian, et al.
Published: (2018-12-01)

A Sparse Underdetermined Blind Source Separation Method and Its Application in Fault Diagnosis of Rotating Machinery
by: HongChao Wang, et al.
Published: (2020-01-01)

Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis
by: Shuangyuan Wang, et al.
Published: (2016-12-01)

Adaptive machinery fault diagnosis based on improved shift-invariant sparse coding
by: Limin Li
Published: (2017-06-01)

Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model
by: Qing Li, et al.
Published: (2018-06-01)

Complex Singular Spectrum Decomposition and its Application to Rotating Machinery Fault Diagnosis
by: Bin Pang, et al.
Published: (2019-01-01)

A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine
by: Ke Li, et al.
Published: (2019-01-01)

Multiple Faults Detection for Rotating Machinery Based on Bicomponent Sparse Low-Rank Matrix Separation Approach
by: Qing Li, et al.
Published: (2018-01-01)

Exploiting sparse markov and covariance structure in multiresolution models
by: Choi, Myung Jin, et al.
Published: (2011)

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis
by: Kai Zheng, et al.
Published: (2020-09-01)

Sparse Representation of Transients Based on Wavelet Basis and Majorization-Minimization Algorithm for Machinery Fault Diagnosis
by: Wei Fan, et al.
Published: (2014-01-01)

Fault diagnosis of rotating machinery
by: Edwards, S.
Published: (1999)

Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions
by: Gye-Bong Jang, et al.
Published: (2021-02-01)

Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure
by: Choi, Myung Jin, et al.
Published: (2010)

Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition
by: Canyi Du, et al.
Published: (2021-01-01)

Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition
by: Yaguo Lei, et al.
Published: (2013-12-01)

Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis
by: Li, S., et al.
Published: (2022)

A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
by: Rubén Ibáñez, et al.
Published: (2018-01-01)

Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal -factor
by: Yan Lu, et al.
Published: (2019-09-01)

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review
by: Wentao Huang, et al.
Published: (2017-06-01)

Sparse decomposition based on ADMM dictionary learning for fault feature extraction of rolling element bearing
by: Qingbin Tong, et al.
Published: (2016-12-01)

Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition
by: Dong Liu, et al.
Published: (2020-01-01)

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition
by: Yuan Xie, et al.
Published: (2017-01-01)

Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis
by: Yonggen Chang, et al.
Published: (2017-03-01)

Matrix probing, skeleton decompositions, and sparse Fourier transform
by: Chiu, Jiawei
Published: (2014)

Application of Hilbert-Huang Transform to the Bearing Fault Diagnosis of Rotating Machinery
by: Han-min Fu, et al.
Published: (2010)

Feature reconstruction based on t-SNE: an approach for fault diagnosis of rotating machinery
by: Jiayu Chen, et al.
Published: (2017-11-01)

Triangular decomposition of sparse matrix
by: ZHOU, LING-YANG, et al.
Published: (1989)

Investigations in Fault Diagnosis Methods for Rotating Machinery
by: Chun-Chieh Wang, et al.

Fault Diagnosis of Vibrational Signature For Rotating Machinery
by: Ching Su Yang, et al.
Published: (1999)

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery
by: Xiaojie Guo, et al.
Published: (2016-12-01)

Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection
by: Zong Yuan, et al.
Published: (2021-01-01)

Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection
by: Dongying Han, et al.
Published: (2020-12-01)

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis
by: Na Lu, et al.
Published: (2019-01-01)

Rank-One and Transformed Sparse Decomposition for Dynamic Cardiac MRI
by: Xianchao Xiu, et al.
Published: (2015-01-01)

Cannot write session to /tmp/vufind_sessions/sess_49n363dejbrufeisu8dnnt1dji