Cargando…

A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals

In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more se...

Descripción completa

Detalles Bibliográficos
Autores principales: Yan, Baokang, Li, Zhiqian, Zhou, Fengqi, Lv, Xu, Zhou, Fengxing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611008/
https://www.ncbi.nlm.nih.gov/pubmed/36298330
http://dx.doi.org/10.3390/s22207973
_version_ 1784819420280913920
author Yan, Baokang
Li, Zhiqian
Zhou, Fengqi
Lv, Xu
Zhou, Fengxing
author_facet Yan, Baokang
Li, Zhiqian
Zhou, Fengqi
Lv, Xu
Zhou, Fengxing
author_sort Yan, Baokang
collection PubMed
description In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more sensitive to frequency and can eliminate the low-frequency harmonic component and noise; secondly, a smoothing filtering algorithm was adopted to suppress the noise in the TEO envelope; thirdly, the fault signal was reconstructed by multiplication of the filtered TEO envelope and the original fault signal; finally, sparse decomposition was used based on a generalized S-transform (GST) to obtain the sparse representation of the signal. The proposed preprocessing method using the filtered TEO can overcome the interference of high-frequency noise while maintaining the structure of fault impulses, which helps the processed signal perform better on sparse decomposition; sparse decomposition based on GST was used to represent the fault signal more quickly and more accurately. Simulation and application prove that the proposed method has good accuracy and efficiency, especially in conditions of very low SNR, such as impulses with anSNR of −8.75 dB that are submerged by noise of the same amplitude.
format Online
Article
Text
id pubmed-9611008
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96110082022-10-28 A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals Yan, Baokang Li, Zhiqian Zhou, Fengqi Lv, Xu Zhou, Fengxing Sensors (Basel) Article In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more sensitive to frequency and can eliminate the low-frequency harmonic component and noise; secondly, a smoothing filtering algorithm was adopted to suppress the noise in the TEO envelope; thirdly, the fault signal was reconstructed by multiplication of the filtered TEO envelope and the original fault signal; finally, sparse decomposition was used based on a generalized S-transform (GST) to obtain the sparse representation of the signal. The proposed preprocessing method using the filtered TEO can overcome the interference of high-frequency noise while maintaining the structure of fault impulses, which helps the processed signal perform better on sparse decomposition; sparse decomposition based on GST was used to represent the fault signal more quickly and more accurately. Simulation and application prove that the proposed method has good accuracy and efficiency, especially in conditions of very low SNR, such as impulses with anSNR of −8.75 dB that are submerged by noise of the same amplitude. MDPI 2022-10-19 /pmc/articles/PMC9611008/ /pubmed/36298330 http://dx.doi.org/10.3390/s22207973 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yan, Baokang
Li, Zhiqian
Zhou, Fengqi
Lv, Xu
Zhou, Fengxing
A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals
title A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals
title_full A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals
title_fullStr A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals
title_full_unstemmed A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals
title_short A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals
title_sort fast sparse decomposition based on the teager energy operator in extraction of weak fault signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611008/
https://www.ncbi.nlm.nih.gov/pubmed/36298330
http://dx.doi.org/10.3390/s22207973
work_keys_str_mv AT yanbaokang afastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT lizhiqian afastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT zhoufengqi afastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT lvxu afastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT zhoufengxing afastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT yanbaokang fastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT lizhiqian fastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT zhoufengqi fastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT lvxu fastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals
AT zhoufengxing fastsparsedecompositionbasedontheteagerenergyoperatorinextractionofweakfaultsignals