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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/20/7973 |
| _version_ | 1797469921884504064 |
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| author | Baokang Yan Zhiqian Li Fengqi Zhou Xu Lv Fengxing Zhou |
| author_facet | Baokang Yan Zhiqian Li Fengqi Zhou Xu Lv Fengxing Zhou |
| author_sort | Baokang Yan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T19:30:48Z |
| format | Article |
| id | doaj.art-86a265a9e2134e898ba1f789df760d8f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:30:48Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-86a265a9e2134e898ba1f789df760d8f2023-11-24T02:29:43ZengMDPI AGSensors1424-82202022-10-012220797310.3390/s22207973A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault SignalsBaokang Yan0Zhiqian Li1Fengqi Zhou2Xu Lv3Fengxing Zhou4Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaSchool of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, ChinaEngineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaEngineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaEngineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, ChinaIn 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.https://www.mdpi.com/1424-8220/22/20/7973fault diagnosisfiltered Teager energy operationsparse decompositionsignal reconstruction |
| spellingShingle | Baokang Yan Zhiqian Li Fengqi Zhou Xu Lv Fengxing Zhou A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals Sensors fault diagnosis filtered Teager energy operation sparse decomposition signal reconstruction |
| 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 | fault diagnosis filtered Teager energy operation sparse decomposition signal reconstruction |
| url | https://www.mdpi.com/1424-8220/22/20/7973 |
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