Adaptive Diagnosis for Transformer with Unknown Faults Based on Antenna-Augmented RFID Sensor and Deep Learning
In engineering practice, transformers always have one or more new mechanical fault types that have not been found yet, which would decrease the diagnosis accuracy. This work introduces an adaptive fault diagnosis approach for transformer mechanical failure in incubation period with unknown fault typ...
Uložené v:
| Vydané v: | IEEE sensors journal Ročník 23; číslo 17; s. 1 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1530-437X, 1558-1748 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | In engineering practice, transformers always have one or more new mechanical fault types that have not been found yet, which would decrease the diagnosis accuracy. This work introduces an adaptive fault diagnosis approach for transformer mechanical failure in incubation period with unknown fault types. An antenna-augmented radio frequency identification (RFID) sensor is used to acquire vibration signal and position information. The faults' signals of transformer are generally featured as nonlinear, moreover, it always contains unknown faults, making it hard to extract discriminative features from the obtained signals. Therefore, an adaptive fault diagnosis method consisting of sparse stacked denoising autoencoder (SSDA) and unknown fault cluster is proposed. The SSDA is adopted to extract robust feature and quantum particle swarm optimization (QPSO) is employed to find the optimal the layer nodes structure and learning rate of the SSDA model. The SSDA model along with a full-connected layer can divide the data into known faults and unknown faults. The knowns faults are classified by support vector machine (SVM), and the unknown faults are divided into different categories by using the fault cluster. The experiments validate that the augmented RFID sensor has reliable communication performance within the distance of 17.5m. Moreover, the proposed method achieves prominent results for transformer fault diagnosis with unknown faults. |
|---|---|
| AbstractList | In engineering practice, transformers always have one or more new mechanical fault types that have not been found yet, which would decrease the diagnosis accuracy. This work introduces an adaptive fault diagnosis approach for transformer mechanical failure in incubation period with unknown fault types. An antenna-augmented radio frequency identification (RFID) sensor is used to acquire vibration signal and position information. The faults’ signals of transformer are generally featured as nonlinear; moreover, it always contains unknown faults, making it hard to extract discriminative features from the obtained signals. Therefore, an adaptive fault diagnosis method consisting of sparse stacked denoising autoencoder (SSDA) and unknown fault cluster is proposed. The SSDA is adopted to extract robust feature and quantum particle swarm optimization (QPSO) is employed to find the optimal the layer node structure (LNS) and learning rate (LR) of the SSDA model. The SSDA model along with a full-connected layer (FCL) can divide the data into known faults and unknown faults. The knowns faults are classified by support vector machine (SVM), and the unknown faults are divided into different categories by using the fault cluster. The experiments validate that the augmented RFID sensor has reliable communication performance within the distance of 17.5 m. Moreover, the proposed method achieves prominent results for transformer fault diagnosis with unknown faults. In engineering practice, transformers always have one or more new mechanical fault types that have not been found yet, which would decrease the diagnosis accuracy. This work introduces an adaptive fault diagnosis approach for transformer mechanical failure in incubation period with unknown fault types. An antenna-augmented radio frequency identification (RFID) sensor is used to acquire vibration signal and position information. The faults' signals of transformer are generally featured as nonlinear, moreover, it always contains unknown faults, making it hard to extract discriminative features from the obtained signals. Therefore, an adaptive fault diagnosis method consisting of sparse stacked denoising autoencoder (SSDA) and unknown fault cluster is proposed. The SSDA is adopted to extract robust feature and quantum particle swarm optimization (QPSO) is employed to find the optimal the layer nodes structure and learning rate of the SSDA model. The SSDA model along with a full-connected layer can divide the data into known faults and unknown faults. The knowns faults are classified by support vector machine (SVM), and the unknown faults are divided into different categories by using the fault cluster. The experiments validate that the augmented RFID sensor has reliable communication performance within the distance of 17.5m. Moreover, the proposed method achieves prominent results for transformer fault diagnosis with unknown faults. |
| Author | Wang, Tao He, Yigang Xiao, Yanxia Li, Bing |
| Author_xml | – sequence: 1 givenname: Tao surname: Wang fullname: Wang, Tao organization: School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, China – sequence: 2 givenname: Yanxia surname: Xiao fullname: Xiao, Yanxia organization: Anhui Heli Co., Ltd, Hefei, China – sequence: 3 givenname: Bing surname: Li fullname: Li, Bing organization: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China – sequence: 4 givenname: Yigang orcidid: 0000-0002-6642-0740 surname: He fullname: He, Yigang organization: School of Electrical Engineering, Wuhan University, Wuhan, China |
| BookMark | eNp9kE1LAzEQhoMoqNUfIHgIeN6aZJsmOVbb-kFR8AO8LelmtkbbSU1SxX_vlvYgHjzNe3ifGeY5JLsYEAg54azLOTPnt4-ju65gouyWwhim-Q454FLqgque3l3nkhW9Ur3sk8OU3hjjRkl1QMLA2WX2n0CH3s4wJJ9oEyJ9ihZTGxYQ6ZfPr_QZ3zF8IR3b1TwnemETOBqQDjADoi0Gq9kC2uzow_hmSB8BU7vGoqNDgCWdgI3ocXZE9ho7T3C8nR3yPB49XV4Xk_urm8vBpKiF6eWiLkFo0y_ltM9qJxunjBJOQFMbJxVT4JS2rm4_r7ViThruhLTTZmr6RmsBZYecbfYuY_hYQcrVW1hFbE9WQkutZOtKti21adUxpBShqWqfbfYBc7R-XnFWre1Wa7vV2m61tduS_A-5jH5h4_e_zOmG8QDwq8-N7vV5-QMbBYgp |
| CODEN | ISJEAZ |
| CitedBy_id | crossref_primary_10_1177_14759217251350560 |
| Cites_doi | 10.1038/s41598-021-83184-2 10.1049/iet-smt.2017.0124 10.1109/ACCESS.2019.2925426 10.1109/TIE.2015.2464297 10.1016/j.ins.2016.02.044 10.1109/TIM.2016.2575318 10.1177/14759217221117101 10.1142/s1793536909000047 10.1109/JSEN.2017.2738028 10.1049/iet-epa.2018.5545 10.1109/JSEN.2022.3203994 10.3390/ma15217851 10.1007/s11042-022-12701-w 10.1155/2020/7407236 10.1109/TIE.2013.2284143 10.15837/ijccc.2020.1.3780 10.1109/JSEN.2017.2687942 10.1109/TIM.2020.3035385 10.1109/TII.2020.2988208 10.1002/stc.3129 10.1109/TPWRD.2005.852280 10.1016/j.jmsy.2022.03.009 10.3390/s22062352 10.1016/j.isatra.2021.06.005 10.1109/TIM.2015.2502922 10.1007/s10489-022-03773-0 10.1109/TIM.2016.2598019 10.1016/j.eswa.2010.12.095 10.1016/j.neucom.2015.07.130 10.1109/TPWRD.2014.2376033 10.1109/tii.2022.3232766 10.1109/JSEN.2018.2844799 10.1109/TIM.2016.2566838 10.1016/j.ymssp.2023.110229 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 7U5 8FD L7M |
| DOI | 10.1109/JSEN.2023.3299081 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Solid State and Superconductivity Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography Engineering |
| EISSN | 1558-1748 |
| EndPage | 1 |
| ExternalDocumentID | 10_1109_JSEN_2023_3299081 10198461 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 51577046; 52167008 funderid: 10.13039/501100001809 – fundername: National Key Research and Development Plan "smart grid technology and equipment" grantid: 2020YFB0905900 |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AGQYO AHBIQ AJQPL AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 EBS F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TWZ AAYXX CITATION 7SP 7U5 8FD L7M |
| ID | FETCH-LOGICAL-c294t-c3e289635b60cd5fd7972d2efc9d5707ed78adc109c870d591d25abfb969882e3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001059669700149&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1530-437X |
| IngestDate | Mon Jun 30 10:11:13 EDT 2025 Sat Nov 29 06:39:38 EST 2025 Tue Nov 18 21:33:21 EST 2025 Wed Aug 27 02:57:14 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 17 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c294t-c3e289635b60cd5fd7972d2efc9d5707ed78adc109c870d591d25abfb969882e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-6642-0740 0009-0003-7350-8649 0009-0009-8693-1079 |
| PQID | 2858752025 |
| PQPubID | 75733 |
| PageCount | 1 |
| ParticipantIDs | crossref_citationtrail_10_1109_JSEN_2023_3299081 proquest_journals_2858752025 crossref_primary_10_1109_JSEN_2023_3299081 ieee_primary_10198461 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-09-01 |
| PublicationDateYYYYMMDD | 2023-09-01 |
| PublicationDate_xml | – month: 09 year: 2023 text: 2023-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE sensors journal |
| PublicationTitleAbbrev | JSEN |
| PublicationYear | 2023 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref35 ref34 ref15 ref37 ref14 ref36 ref11 ref33 ref10 ref32 ref2 ref1 ref17 (ref38) 2008 ref16 ref19 ref18 (ref30) 2012 Wang (ref12) 2018; 13 ref24 ref23 ref26 ref25 ref20 ref22 ref21 Vincent (ref31) 2010; 11 ref28 ref27 ref29 ref7 Gharehpetian (ref8) 2023 ref9 ref4 ref3 ref6 ref5 Hardin (ref39) 1997; 63 ref40 |
| References_xml | – ident: ref23 doi: 10.1038/s41598-021-83184-2 – ident: ref2 doi: 10.1049/iet-smt.2017.0124 – ident: ref5 doi: 10.1109/ACCESS.2019.2925426 – ident: ref19 doi: 10.1109/TIE.2015.2464297 – ident: ref20 doi: 10.1016/j.ins.2016.02.044 – ident: ref18 doi: 10.1109/TIM.2016.2575318 – ident: ref13 doi: 10.1177/14759217221117101 – ident: ref34 doi: 10.1142/s1793536909000047 – ident: ref10 doi: 10.1109/JSEN.2017.2738028 – ident: ref3 doi: 10.1049/iet-epa.2018.5545 – volume: 11 start-page: 3371 year: 2010 ident: ref31 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – ident: ref4 doi: 10.1109/JSEN.2022.3203994 – ident: ref7 doi: 10.3390/ma15217851 – volume-title: SMS7630 Series Surface-Mount Schottky Diodes year: 2012 ident: ref30 – ident: ref22 doi: 10.1007/s11042-022-12701-w – ident: ref32 doi: 10.1155/2020/7407236 – ident: ref15 doi: 10.1109/TIE.2013.2284143 – ident: ref26 doi: 10.15837/ijccc.2020.1.3780 – volume: 63 start-page: 735 issue: 6 year: 1997 ident: ref39 article-title: Statistical significance and normalized confusion matrices publication-title: Photogramm. Eng. Remote Sens. – ident: ref1 doi: 10.1109/JSEN.2017.2687942 – ident: ref28 doi: 10.1109/TIM.2020.3035385 – ident: ref29 doi: 10.1109/TII.2020.2988208 – ident: ref25 doi: 10.1002/stc.3129 – ident: ref37 doi: 10.1109/TPWRD.2005.852280 – ident: ref35 doi: 10.1016/j.jmsy.2022.03.009 – ident: ref6 doi: 10.3390/s22062352 – ident: ref24 doi: 10.1016/j.isatra.2021.06.005 – ident: ref17 doi: 10.1109/TIM.2015.2502922 – volume: 13 start-page: 2134 issue: 5 year: 2018 ident: ref12 article-title: Fault diagnosis of transformer based on self-powered RFID sensor tag and improved HHT publication-title: J. Elect. Eng. Technol. – ident: ref40 doi: 10.1007/s10489-022-03773-0 – ident: ref14 doi: 10.1109/TIM.2016.2598019 – volume-title: EPC Radio-Frequency Identity Protocols Generation-2 UHF RFID year: 2008 ident: ref38 – volume-title: Power Transformer Online Monitoring Using Electromagnetic Waves year: 2023 ident: ref8 – ident: ref33 doi: 10.1016/j.eswa.2010.12.095 – ident: ref21 doi: 10.1016/j.neucom.2015.07.130 – ident: ref36 doi: 10.1109/TPWRD.2014.2376033 – ident: ref27 doi: 10.1109/tii.2022.3232766 – ident: ref11 doi: 10.1109/JSEN.2018.2844799 – ident: ref16 doi: 10.1109/TIM.2016.2566838 – ident: ref9 doi: 10.1016/j.ymssp.2023.110229 |
| SSID | ssj0019757 |
| Score | 2.3946223 |
| Snippet | In engineering practice, transformers always have one or more new mechanical fault types that have not been found yet, which would decrease the diagnosis... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | adaptive fault diagnosis antenna-augmented RFID sensor Antennas cluster Clusters Deep learning Fault diagnosis Faults Feature extraction Machine learning Particle swarm optimization Power transformers Radio frequency identification Radiofrequency identification Sensors sparse stacked denoising autoencoder Support vector machines transformer Transformers Vibrations |
| Title | Adaptive Diagnosis for Transformer with Unknown Faults Based on Antenna-Augmented RFID Sensor and Deep Learning |
| URI | https://ieeexplore.ieee.org/document/10198461 https://www.proquest.com/docview/2858752025 |
| Volume | 23 |
| WOSCitedRecordID | wos001059669700149&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-1748 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019757 issn: 1530-437X databaseCode: RIE dateStart: 20010101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LaxsxEBapCbQ9NGmaUrdO0KGnwLr7krU6urVNEoIJsV18WyTN2DWYtfGj0H_fkVYOCaWF3vagEYs-zUvzYuxzRuLQkuyPjESIclfEVaRJHs2gkFZipo2Pnn-_k8NhMZ2q-1Cs7mthENEnn2HbffpYPqzs3j2VEYeTi5w7Z-eFlLIu1noMGSjp23oSB8dRnslpCGEmsfpyO-oP225OeDtz0rdInikhP1XlD1Hs9cvg5D__7JS9CYYk79bIv2VHWJ2x10_aC56xl2HC-Y9f79iqC3rtRBvv1cl1iy0ne5WPD4Yrbrh7k-WTyj2zVXyg98vdln8lLQd8VfGuy3WvdNTdz30fT-APg5seH5EbTNvoCngPcc1Dv9b5OZsM-uNv11EYthDZVOW7yGZIvheZH6YTWxAzkEqmkOLMKhAyJiRlocHScVpicRAqgVRoMzOqo8hKx-w9a1SrCj8wnnd0B5IYQGCWp1oUmQZjLRoyzYRJTZPFh9MvbehE7gZiLEvvkcSqdICVDrAyANZkV48k67oNx78WnzuEniyswWmy1gHjMnDqtkwLQS4bkYuPfyH7xF653evEshZr7DZ7vGDH9udusd1c-kv4G0-r2XE |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Nb9MwFH9CA2lwYDA2UdjAB05IKYlj1_Gx0FUblAqxDvUW2X7umDSlVT-Q-O95dtxpaGIStxz8ksg_vy-_L4B3JYlDR7I_s8pjJkIRV8ULkc2wUk750tgYPf8xUuNxNZ3qb6lYPdbCeO9j8pnvhscYy8e524SrMuJwcpFFcHYeSiF40ZZr3QQNtIqNPYmH80yUapqCmEWuP3w-Pxl3w6Twbhnkb1X8pYbiXJU7wjhqmOHef_7bM3iaTEnWb7F_Dg98sw9PbjUY3IfdNOP85-8XMO-jWQThxgZtet3VipHFyiZb09UvWbiVZRdNuGhr2NBsrtcr9pH0HLJ5w_oh270xWX9zGTt5Ivs-PBuwc3KE6TWmQTbwfsFSx9bLA7gYnkw-nWZp3ELmuBbrzJWevC8yQGwvdyhnqLTiyP3MaZQqJyxVZdDRdjpicpS6QC6NnVnd02Sn-_IQdpp5418CEz3TwyJHlL4U3MiqNGid85aMM2m57UC-3f3apV7kYSTGdR19klzXAbA6AFYnwDrw_oZk0TbiuG_xQUDo1sIWnA4cbTGuE6-ual5JctqIXL76B9lb2D2dfB3Vo7Pxl9fwOHypTTM7gp31cuOP4ZH7tb5aLd_EA_kHxbfcuA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Adaptive+Diagnosis+for+Transformer+With+Unknown+Faults+Based+on+Antenna-Augmented+RFID+Sensor+and+Deep+Learning&rft.jtitle=IEEE+sensors+journal&rft.au=Wang%2C+Tao&rft.au=Xiao%2C+Yanxia&rft.au=Li%2C+Bing&rft.au=He%2C+Yigang&rft.date=2023-09-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1530-437X&rft.eissn=1558-1748&rft.volume=23&rft.issue=17&rft.spage=20423&rft_id=info:doi/10.1109%2FJSEN.2023.3299081&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1530-437X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1530-437X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1530-437X&client=summon |