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: Lixiang Duan, Yangshen Wang, Jinjiang Wang, Laibin Zhang, Jinglong Chen
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2016/9792807
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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.
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id doaj-art-2f4ab7268b124a0f8ce6df2d438ea56d
institution OA Journals
issn 1070-9622
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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
work_keys_str_mv AT lixiangduan undecimatedliftingwaveletpackettransformwithboundarytreatmentformachineryincipientfaultdiagnosis
AT yangshenwang undecimatedliftingwaveletpackettransformwithboundarytreatmentformachineryincipientfaultdiagnosis
AT jinjiangwang undecimatedliftingwaveletpackettransformwithboundarytreatmentformachineryincipientfaultdiagnosis
AT laibinzhang undecimatedliftingwaveletpackettransformwithboundarytreatmentformachineryincipientfaultdiagnosis
AT jinglongchen undecimatedliftingwaveletpackettransformwithboundarytreatmentformachineryincipientfaultdiagnosis