Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis
Effective signal processing in fault detection and diagnosis (FDD) is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet pack...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/9792807 |
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| _version_ | 1850216870664208384 |
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| author | Lixiang Duan Yangshen Wang Jinjiang Wang Laibin Zhang Jinglong Chen |
| author_facet | Lixiang Duan Yangshen Wang Jinjiang Wang Laibin Zhang Jinglong Chen |
| author_sort | Lixiang Duan |
| collection | DOAJ |
| description | Effective signal processing in fault detection and diagnosis (FDD) is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet packet transform (ULWPT) is investigated for machinery incipient fault diagnosis. Undecimated lifting wavelet packet transform is firstly formulated to eliminate the frequency aliasing issue in traditional lifting wavelet packet transform. Next, Volterra series, as a boundary treatment method, is used to preprocess the signal to suppress the end distortion in undecimated lifting wavelet packet transform. Finally, the decomposed wavelet coefficients are trimmed to the original length as the signal of interest for machinery incipient fault detection. Experimental study on a reciprocating compressor is performed to demonstrate the effectiveness of the presented method. The results show that the presented method outperforms the conventional approach by dramatically enhancing the weak defect feature extraction for reciprocating compressor valve fault diagnosis. |
| format | Article |
| id | doaj-art-2f4ab7268b124a0f8ce6df2d438ea56d |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-2f4ab7268b124a0f8ce6df2d438ea56d2025-08-20T02:08:12ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/97928079792807Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault DiagnosisLixiang Duan0Yangshen Wang1Jinjiang Wang2Laibin Zhang3Jinglong Chen4School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaSchool of Material Science and Engineering, Xian University of Architecture and Technology, Xi’an 710055, ChinaEffective signal processing in fault detection and diagnosis (FDD) is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet packet transform (ULWPT) is investigated for machinery incipient fault diagnosis. Undecimated lifting wavelet packet transform is firstly formulated to eliminate the frequency aliasing issue in traditional lifting wavelet packet transform. Next, Volterra series, as a boundary treatment method, is used to preprocess the signal to suppress the end distortion in undecimated lifting wavelet packet transform. Finally, the decomposed wavelet coefficients are trimmed to the original length as the signal of interest for machinery incipient fault detection. Experimental study on a reciprocating compressor is performed to demonstrate the effectiveness of the presented method. The results show that the presented method outperforms the conventional approach by dramatically enhancing the weak defect feature extraction for reciprocating compressor valve fault diagnosis.http://dx.doi.org/10.1155/2016/9792807 |
| spellingShingle | Lixiang Duan Yangshen Wang Jinjiang Wang Laibin Zhang Jinglong Chen Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis Shock and Vibration |
| title | Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis |
| title_full | Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis |
| title_fullStr | Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis |
| title_full_unstemmed | Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis |
| title_short | Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis |
| title_sort | undecimated lifting wavelet packet transform with boundary treatment for machinery incipient fault diagnosis |
| url | http://dx.doi.org/10.1155/2016/9792807 |
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