A rolling bearing fault detection method based on compressed sensing and a neural network
type: Item
instance of: scholarly article
title: A rolling bearing fault detection method based on compressed sensing and a neural network
zbMATH Open document ID: 1470.94063
author: Lu Lu
author: Jiyou Fei
author: Ling Yu
author: Y. Yuan
published in: Mathematical Biosciences and Engineering
publication date: 2021-08-04T00:00:00Z
Mathematics Subject Classification ID: 94A13
Mathematics Subject Classification ID: 94A12
Mathematics Subject Classification ID: 94A20
Mathematics Subject Classification ID: 68T07
zbMATH DE Number: 7378524
zbMATH Keywords: compressed sensing
zbMATH Keywords: neural network
zbMATH Keywords: fault detection
zbMATH Keywords: feature extraction
MaRDI profile type: Publication
full work available at URL: mbe.2020313
OpenAlex ID: W3081592516
Wikidata QID: Q101116916
cites work: Compressed sensing
cites work: Q5008374
cites work: Time-Delay Estimation From Low-Rate Samples: A Union of Subspaces Approach
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cites work: Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property
cites work: Subspace Pursuit for Compressive Sensing Signal Reconstruction
DOI: 10.3934/MBE.2020313
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description: scientific article; zbMATH DE number 7378524