Loading…

Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions

Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring f...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-01, Vol.18 (1), p.150
Main Authors: Hu, Yue, Tu, Xiaotong, Li, Fucai, Meng, Guang
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c469t-e97da4b76eb4042042009e98eeeaf5b54294f63e129e3e19a77e1f92eb411b453
cites cdi_FETCH-LOGICAL-c469t-e97da4b76eb4042042009e98eeeaf5b54294f63e129e3e19a77e1f92eb411b453
container_end_page
container_issue 1
container_start_page 150
container_title Sensors (Basel, Switzerland)
container_volume 18
creator Hu, Yue
Tu, Xiaotong
Li, Fucai
Meng, Guang
description Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.
doi_str_mv 10.3390/s18010150
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_d8a1917972dc4e72bb0025618f6edc57</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_d8a1917972dc4e72bb0025618f6edc57</doaj_id><sourcerecordid>2002811365</sourcerecordid><originalsourceid>FETCH-LOGICAL-c469t-e97da4b76eb4042042009e98eeeaf5b54294f63e129e3e19a77e1f92eb411b453</originalsourceid><addsrcrecordid>eNpdkttu1DAQhiMEoge44AWQJW7gIuBDYsc3SNXSEyoUiUW9tJxkkvUqsRfbqVqeiYfEYctqi2SN7fGnX79nJsteEfyeMYk_BFJhgkmJn2SHpKBFXlGKn-6dD7KjENYYU8ZY9Tw7oJIRznl1mP3-7IyN6ML0q_zat-DR93vbrLwLPyeAX8b2aOm1DZ3zI9K2RV-mIZp8qTcJPR03xptGD-hG38IAcY9NAZ3pBKNPRvfWBROQ69CNSRrLydfGAvo2aAtR-3t0DtrX7g5Ndrbw1dkQdTTOzm8LZ1szX8KL7FmnhwAvH_bj7MfZ6XJxkV9dn18uTq7ypuAy5iBFq4tacKgLXNB5YQmyAgDdlXVZUFl0nAGhElKUWgggnaQJJ6QuSnacXW51W6fXauPNmHwop436m3C-V9pH0wyg2koTSYQUtG0KELSuU5VLTqqOQ9uUIml93GptpnpMKbDR6-GR6OMXa1aqd7eqFLIUJUkCbx8EvEs9CVGNJjQwzLVzU1BEVrLkFRc4oW_-Q9du8jaVSqUS0IoQxuffvdtSTepy8NDtzBCs5nlSu3lK7Ot99zvy3wCxP3NPyLs</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2002811365</pqid></control><display><type>article</type><title>Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions</title><source>Publicly Available Content (ProQuest)</source><source>PubMed Central</source><creator>Hu, Yue ; Tu, Xiaotong ; Li, Fucai ; Meng, Guang</creator><creatorcontrib>Hu, Yue ; Tu, Xiaotong ; Li, Fucai ; Meng, Guang</creatorcontrib><description>Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s18010150</identifier><identifier>PMID: 29316668</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Critical components ; Downtime ; empirical wavelet transform ; Fault diagnosis ; Gear trains ; Masking ; Meshing ; multi-taper ; synchrosqueezing transform ; time-frequency analysis ; Variation ; Vibration measurement ; Wavelet transforms ; wind turbine gearbox</subject><ispartof>Sensors (Basel, Switzerland), 2018-01, Vol.18 (1), p.150</ispartof><rights>2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 by the authors. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-e97da4b76eb4042042009e98eeeaf5b54294f63e129e3e19a77e1f92eb411b453</citedby><cites>FETCH-LOGICAL-c469t-e97da4b76eb4042042009e98eeeaf5b54294f63e129e3e19a77e1f92eb411b453</cites><orcidid>0000-0002-5365-2593 ; 0000-0002-7190-2429</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2002811365/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2002811365?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,25734,27905,27906,36993,36994,44571,53772,53774,74875</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29316668$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hu, Yue</creatorcontrib><creatorcontrib>Tu, Xiaotong</creatorcontrib><creatorcontrib>Li, Fucai</creatorcontrib><creatorcontrib>Meng, Guang</creatorcontrib><title>Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions</title><title>Sensors (Basel, Switzerland)</title><addtitle>Sensors (Basel)</addtitle><description>Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.</description><subject>Critical components</subject><subject>Downtime</subject><subject>empirical wavelet transform</subject><subject>Fault diagnosis</subject><subject>Gear trains</subject><subject>Masking</subject><subject>Meshing</subject><subject>multi-taper</subject><subject>synchrosqueezing transform</subject><subject>time-frequency analysis</subject><subject>Variation</subject><subject>Vibration measurement</subject><subject>Wavelet transforms</subject><subject>wind turbine gearbox</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkttu1DAQhiMEoge44AWQJW7gIuBDYsc3SNXSEyoUiUW9tJxkkvUqsRfbqVqeiYfEYctqi2SN7fGnX79nJsteEfyeMYk_BFJhgkmJn2SHpKBFXlGKn-6dD7KjENYYU8ZY9Tw7oJIRznl1mP3-7IyN6ML0q_zat-DR93vbrLwLPyeAX8b2aOm1DZ3zI9K2RV-mIZp8qTcJPR03xptGD-hG38IAcY9NAZ3pBKNPRvfWBROQ69CNSRrLydfGAvo2aAtR-3t0DtrX7g5Ndrbw1dkQdTTOzm8LZ1szX8KL7FmnhwAvH_bj7MfZ6XJxkV9dn18uTq7ypuAy5iBFq4tacKgLXNB5YQmyAgDdlXVZUFl0nAGhElKUWgggnaQJJ6QuSnacXW51W6fXauPNmHwop436m3C-V9pH0wyg2koTSYQUtG0KELSuU5VLTqqOQ9uUIml93GptpnpMKbDR6-GR6OMXa1aqd7eqFLIUJUkCbx8EvEs9CVGNJjQwzLVzU1BEVrLkFRc4oW_-Q9du8jaVSqUS0IoQxuffvdtSTepy8NDtzBCs5nlSu3lK7Ot99zvy3wCxP3NPyLs</recordid><startdate>20180107</startdate><enddate>20180107</enddate><creator>Hu, Yue</creator><creator>Tu, Xiaotong</creator><creator>Li, Fucai</creator><creator>Meng, Guang</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-5365-2593</orcidid><orcidid>https://orcid.org/0000-0002-7190-2429</orcidid></search><sort><creationdate>20180107</creationdate><title>Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions</title><author>Hu, Yue ; Tu, Xiaotong ; Li, Fucai ; Meng, Guang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c469t-e97da4b76eb4042042009e98eeeaf5b54294f63e129e3e19a77e1f92eb411b453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Critical components</topic><topic>Downtime</topic><topic>empirical wavelet transform</topic><topic>Fault diagnosis</topic><topic>Gear trains</topic><topic>Masking</topic><topic>Meshing</topic><topic>multi-taper</topic><topic>synchrosqueezing transform</topic><topic>time-frequency analysis</topic><topic>Variation</topic><topic>Vibration measurement</topic><topic>Wavelet transforms</topic><topic>wind turbine gearbox</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Yue</creatorcontrib><creatorcontrib>Tu, Xiaotong</creatorcontrib><creatorcontrib>Li, Fucai</creatorcontrib><creatorcontrib>Meng, Guang</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Sensors (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Yue</au><au>Tu, Xiaotong</au><au>Li, Fucai</au><au>Meng, Guang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions</atitle><jtitle>Sensors (Basel, Switzerland)</jtitle><addtitle>Sensors (Basel)</addtitle><date>2018-01-07</date><risdate>2018</risdate><volume>18</volume><issue>1</issue><spage>150</spage><pages>150-</pages><issn>1424-8220</issn><eissn>1424-8220</eissn><abstract>Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>29316668</pmid><doi>10.3390/s18010150</doi><orcidid>https://orcid.org/0000-0002-5365-2593</orcidid><orcidid>https://orcid.org/0000-0002-7190-2429</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1424-8220
ispartof Sensors (Basel, Switzerland), 2018-01, Vol.18 (1), p.150
issn 1424-8220
1424-8220
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_d8a1917972dc4e72bb0025618f6edc57
source Publicly Available Content (ProQuest); PubMed Central
subjects Critical components
Downtime
empirical wavelet transform
Fault diagnosis
Gear trains
Masking
Meshing
multi-taper
synchrosqueezing transform
time-frequency analysis
Variation
Vibration measurement
Wavelet transforms
wind turbine gearbox
title Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T20%3A55%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Joint%20High-Order%20Synchrosqueezing%20Transform%20and%20Multi-Taper%20Empirical%20Wavelet%20Transform%20for%20Fault%20Diagnosis%20of%20Wind%20Turbine%20Planetary%20Gearbox%20under%20Nonstationary%20Conditions&rft.jtitle=Sensors%20(Basel,%20Switzerland)&rft.au=Hu,%20Yue&rft.date=2018-01-07&rft.volume=18&rft.issue=1&rft.spage=150&rft.pages=150-&rft.issn=1424-8220&rft.eissn=1424-8220&rft_id=info:doi/10.3390/s18010150&rft_dat=%3Cproquest_doaj_%3E2002811365%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c469t-e97da4b76eb4042042009e98eeeaf5b54294f63e129e3e19a77e1f92eb411b453%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2002811365&rft_id=info:pmid/29316668&rfr_iscdi=true