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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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 |
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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 |
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