Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors
Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much sl...
Uloženo v:
| Vydáno v: | Quality and reliability engineering international Ročník 40; číslo 2; s. 819 - 837 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Bognor Regis
Wiley Subscription Services, Inc
01.03.2024
|
| Témata: | |
| ISSN: | 0748-8017, 1099-1638 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much slighter fluctuations to the performance of drive circuits. If the two fault modes co‐exist, traditional fault diagnosis models are prone to misclassify soft faults as the normal condition. To improve the accuracy of fault diagnosis of drive circuits, it necessitates to accurately locate the faults of drive circuits, while effectively extracting the distinguishable fault features is one of the critical factors for fault location. In this article, a fault location method combining the empirical modal decomposition (EMD) algorithm and sparse convolutional autoencoder (SCAE) is proposed. The EMD algorithm is applied to decompose the three‐phase current signals of drive circuits. An SCAE‐based feature extractor is constructed to capture high‐dimensional and sparse fault feature data with the aid of the powerful feature autonomic extraction capability of deep learning. A deep classifier is designed to locate faults in the driver circuit. A fault simulation model of the drive circuit is developed and the monitor data is collected. The effectiveness of the proposed method is validated via a real case of drive circuit in nuclear reactors. |
|---|---|
| AbstractList | Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much slighter fluctuations to the performance of drive circuits. If the two fault modes co‐exist, traditional fault diagnosis models are prone to misclassify soft faults as the normal condition. To improve the accuracy of fault diagnosis of drive circuits, it necessitates to accurately locate the faults of drive circuits, while effectively extracting the distinguishable fault features is one of the critical factors for fault location. In this article, a fault location method combining the empirical modal decomposition (EMD) algorithm and sparse convolutional autoencoder (SCAE) is proposed. The EMD algorithm is applied to decompose the three‐phase current signals of drive circuits. An SCAE‐based feature extractor is constructed to capture high‐dimensional and sparse fault feature data with the aid of the powerful feature autonomic extraction capability of deep learning. A deep classifier is designed to locate faults in the driver circuit. A fault simulation model of the drive circuit is developed and the monitor data is collected. The effectiveness of the proposed method is validated via a real case of drive circuit in nuclear reactors. |
| Author | Yang, Cheng Wang, Fu Zhang, Qiang Yuan, Yannan Li, Jueying Li, Ang Min, Yuan |
| Author_xml | – sequence: 1 givenname: Cheng surname: Yang fullname: Yang, Cheng organization: Nuclear Power Institute of China – sequence: 2 givenname: Yannan surname: Yuan fullname: Yuan, Yannan email: yannan_yuan@163.com organization: Nuclear Power Institute of China – sequence: 3 givenname: Fu surname: Wang fullname: Wang, Fu organization: University of Electronic Science and Technology of China – sequence: 4 givenname: Jueying surname: Li fullname: Li, Jueying organization: Nuclear Power Institute of China – sequence: 5 givenname: Ang surname: Li fullname: Li, Ang organization: Nuclear Power Institute of China – sequence: 6 givenname: Yuan orcidid: 0000-0001-7771-1984 surname: Min fullname: Min, Yuan organization: Nuclear Power Institute of China – sequence: 7 givenname: Qiang surname: Zhang fullname: Zhang, Qiang organization: Civil Aviation Flight University of China |
| BookMark | eNp10MtKAzEYBeAgCrZV8BECbtxMzSQznWQppV6gIN62hn8yGUiJkzbJVLrzEXxGn8S0dSW6Shbf-eGcITrsXKcROsvJOCeEXq68HrOipAdokBMhsnzC-CEakKrgGSd5dYyGISwISVjwAXp9WoIPGivXrZ3to3EdWAx9dLpTrtH-6-OzhqAb3EJvI7ZOwRbh1nnceLNOUeNVb2LApsNdr6wGj70GFZ0PJ-ioBRv06c87Qi_Xs-fpbTa_v7mbXs0zRQWj2YSUBedAdAN1-jRlDqwQNRGgoCpazapasVaxSSpTli1AQ0hFdaUpQEknwEbofH936d2q1yHKhet9qhIkFZQWJRecJTXeK-VdCF63Upm4qxM9GCtzIrcbyrSh3G6YAhe_Aktv3sBv_qLZnr4bqzf_OvnwONv5b1mmhOk |
| CitedBy_id | crossref_primary_10_1016_j_ress_2024_110198 crossref_primary_10_1002_qre_3580 |
| Cites_doi | 10.1109/TPEL.2009.2017082 10.1016/j.advengsoft.2022.103354 10.1016/j.ress.2022.108561 10.1016/j.ress.2023.109120 10.1007/s42524‐023‐0256‐2 10.1080/15325008.2020.1793835 10.1109/TPEL.2012.2192503 10.1016/j.rser.2017.03.021 10.1109/TPEL.2013.2242093 10.1002/qre.3172 10.1109/TIE.2018.2833045 10.1109/TPEL.2021.3122816 10.1109/TIE.2014.2348934 10.1016/j.ijepes.2019.105425 10.1016/j.asoc.2016.11.012 10.1109/TIE.2009.2013845 10.1002/qre.315 10.1109/TIE.2017.2777378 10.1121/1.395916 10.1109/TIE.2014.2375853 10.1109/TPEL.2016.2526684 10.1098/rspa.1998.0193 10.1109/5.4428 10.1109/63.575672 10.1109/TPEL.2014.2348194 10.1109/TPWRD.2022.3223410 10.1016/j.renene.2022.08.080 10.1109/5.931486 10.1109/TPEL.2019.2941129 10.1016/j.measurement.2016.04.007 10.1007/s42524-021-0169-x 10.1002/qre.3319 10.1109/TIA.2021.3123199 10.1207/s15516709cog0901_7 10.1109/TPEL.2017.2679439 10.1002/qre.3280 10.1109/TIE.2022.3153810 10.1016/j.ymssp.2022.109628 10.1109/TIE.2020.3028808 |
| ContentType | Journal Article |
| Copyright | 2023 John Wiley & Sons Ltd. 2024 John Wiley & Sons Ltd. |
| Copyright_xml | – notice: 2023 John Wiley & Sons Ltd. – notice: 2024 John Wiley & Sons Ltd. |
| DBID | AAYXX CITATION 7TB 8FD FR3 |
| DOI | 10.1002/qre.3452 |
| DatabaseName | CrossRef Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database |
| DatabaseTitle | CrossRef Technology Research Database Mechanical & Transportation Engineering Abstracts Engineering Research Database |
| DatabaseTitleList | Technology Research Database CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1099-1638 |
| EndPage | 837 |
| ExternalDocumentID | 10_1002_qre_3452 QRE3452 |
| Genre | article |
| GroupedDBID | .3N .GA .Y3 05W 0R~ 10A 123 1L6 1OB 1OC 31~ 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8WZ 930 A03 A6W AAESR AAEVG AAHHS AAHQN AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ABJNI ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACGFS ACIWK ACPOU ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADMGS ADNMO ADOZA ADXAS AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFNX AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CMOOK CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EBS EJD ESX F00 F01 F04 FEDTE G-S G.N GNP GODZA H.T H.X HBH HF~ HGLYW HHY HVGLF HZ~ IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M59 MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- P2P P2W P2X P4D PALCI Q.N Q11 QB0 QRW R.K RIWAO RJQFR RNS ROL RWI RX1 RYL SAMSI SUPJJ TN5 UB1 V2E W8V W99 WBKPD WH7 WIH WIK WLBEL WOHZO WQJ WRC WWI WXSBR WYISQ XG1 XPP XV2 ZZTAW ~IA ~WT AAMMB AAYXX ADMLS AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AIDQK AIDYY AIQQE CITATION O8X 7TB 8FD FR3 |
| ID | FETCH-LOGICAL-c2932-605488a0edab488d51a349b09aca74fe37bc3fc3663855faad0072e7e2aa526a3 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001069740900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0748-8017 |
| IngestDate | Mon Sep 29 02:33:13 EDT 2025 Tue Nov 18 20:49:31 EST 2025 Sat Nov 29 02:33:18 EST 2025 Wed Jan 22 16:14:48 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2932-605488a0edab488d51a349b09aca74fe37bc3fc3663855faad0072e7e2aa526a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-7771-1984 |
| PQID | 2922458983 |
| PQPubID | 1016437 |
| PageCount | 19 |
| ParticipantIDs | proquest_journals_2922458983 crossref_citationtrail_10_1002_qre_3452 crossref_primary_10_1002_qre_3452 wiley_primary_10_1002_qre_3452_QRE3452 |
| PublicationCentury | 2000 |
| PublicationDate | March 2024 2024-03-00 20240301 |
| PublicationDateYYYYMMDD | 2024-03-01 |
| PublicationDate_xml | – month: 03 year: 2024 text: March 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Bognor Regis |
| PublicationPlace_xml | – name: Bognor Regis |
| PublicationTitle | Quality and reliability engineering international |
| PublicationYear | 2024 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 2021; 8 2009; 24 2022; 198 2013; 28 2023; 39 2022; 70 2012 2023; 183 2019; 35 1985; 9 2017; 65 1988; 76 2016; 32 2014; 62 2018; 66 2001; 89 1998; 454 2021; 58 2009; 56 2017; 50 2017; 74 2021; 37 2023 2022 2017; 33 2023; 233 1997; 12 2023; 176 2020; 115 2020; 48 2020; 68 1988; 83 2012; 28 2015 2014; 30 2022; 38 2022; 225 2016; 89 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 Yu F (e_1_2_7_39_1) 2015 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_41_1 e_1_2_7_14_1 e_1_2_7_42_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 e_1_2_7_28_1 e_1_2_7_29_1 Hinton GE (e_1_2_7_40_1) 2012 e_1_2_7_30_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_22_1 e_1_2_7_34_1 e_1_2_7_21_1 e_1_2_7_35_1 e_1_2_7_20_1 e_1_2_7_36_1 e_1_2_7_37_1 e_1_2_7_38_1 |
| References_xml | – volume: 32 start-page: 39 issue: 1 year: 2016 end-page: 51 article-title: Reliable modular multilevel converter fault detection with redundant voltage sensor publication-title: IEEE Trans Power Electron – volume: 66 start-page: 8760 issue: 11 year: 2018 end-page: 8771 article-title: Real‐time fault detection and identification for MMC using 1‐D convolutional neural networks publication-title: IEEE Trans Ind Electron – volume: 89 start-page: 898 issue: 6 year: 2001 end-page: 912 article-title: Modeling and simulation of power electronic converters – year: 2022 article-title: Health status assessment of radar systems at aerospace launch sites by fuzzy analytic hierarchy process publication-title: Qual Reliab Eng Int – volume: 30 start-page: 2721 issue: 5 year: 2014 end-page: 2732 article-title: Fault detection and localization method for modular multilevel converters publication-title: IEEE Trans Power Electron – volume: 8 start-page: 572 issue: 4 year: 2021 end-page: 581 article-title: Novel interpretable mechanism of neural networks based on network decoupling method publication-title: Front Eng Manag – volume: 58 start-page: 457 issue: 1 year: 2021 end-page: 467 article-title: An open‐circuit fault detection and localization scheme for cascaded H‐bridge multilevel converter without additional sensors publication-title: IEEE Trans Ind Appl – volume: 24 start-page: 1859 issue: 8 year: 2009 end-page: 1875 article-title: A review of the state of the art of power electronics for wind turbines publication-title: IEEE Trans Power Electron – volume: 454 start-page: 903 issue: 1971 year: 1998 end-page: 995 article-title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non‐stationary time series analysis – volume: 225 year: 2022 article-title: A multi‐layer spiking neural network‐based approach to bearing fault diagnosis publication-title: Reliab Eng Syst Saf – year: 2015 article-title: Multi‐scale context aggregation by dilated convolutions publication-title: arXiv preprint arXiv – volume: 62 start-page: 1921 issue: 3 year: 2014 end-page: 1930 article-title: Kalman‐filter‐based indicator for online interturn short circuits detection in permanent‐magnet synchronous generators publication-title: IEEE Trans Ind Electron – volume: 28 start-page: 4867 issue: 11 year: 2013 end-page: 4872 article-title: Fault detection for modular multilevel converters based on sliding mode observer publication-title: IEEE Trans Power Electron – volume: 70 start-page: 112 issue: 1 year: 2022 end-page: 124 article-title: A novel detection and localization approach of open‐circuit switch fault for the grid‐connected modular multilevel converter publication-title: IEEE Trans Ind Electron – volume: 39 start-page: 1863 year: 2023 end-page: 1877 article-title: An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge publication-title: Qual Reliab Eng Int – volume: 39 start-page: 1681 year: 2023 end-page: 1703 article-title: Evidential network‐based system reliability assessment by fusing multi‐source evidential information publication-title: Qual Reliab Eng Int – volume: 83 start-page: 1615 issue: 4 year: 1988 end-page: 1626 article-title: Learning the hidden structure of speech publication-title: J Acoust Soc Am – volume: 76 start-page: 428 issue: 4 year: 1988 end-page: 437 article-title: Power electronic circuit topology – year: 2012 article-title: Improving neural networks by preventing co‐adaptation of feature detectors publication-title: arXiv preprint arXiv – volume: 183 year: 2023 article-title: Bayesian transfer learning with active querying for intelligent cross‐machine fault prognosis under limited data publication-title: Mech Syst Sig Process – volume: 33 start-page: 1597 issue: 2 year: 2017 end-page: 1608 article-title: A submodule fault ride‐through strategy for modular multilevel converters with nearest level modulation publication-title: IEEE Trans Power Electron – volume: 198 start-page: 936 year: 2022 end-page: 946 article-title: Hybrid Gaussian Process Regression and Fuzzy Inference System based approach for condition monitoring at the rotor side of a doubly fed induction generator publication-title: Renewable Energy – volume: 56 start-page: 2275 issue: 6 year: 2009 end-page: 2283 article-title: Fault detection on multicell converter based on output voltage frequency analysis publication-title: IEEE Trans Ind Electron – volume: 9 start-page: 147 issue: 1 year: 1985 end-page: 169 article-title: A learning algorithm for Boltzmann machines publication-title: Cogn Sci – year: 2022 article-title: A model of software fault detection and correction processes considering heterogeneous faults publication-title: Qual Reliab Eng Int – volume: 74 start-page: 949 year: 2017 end-page: 958 article-title: Fault location and detection techniques in power distribution systems with distributed generation: a review publication-title: Renewable Sustainable Energy Rev – volume: 233 year: 2023 article-title: Reliability modeling of modular ‐out‐of‐ systems with functional dependency: a case study of radar transmitter systems publication-title: Reliab Eng Syst Saf – volume: 115 year: 2020 article-title: Detection and location of open‐circuit fault for modular multilevel converter publication-title: Int J Electr Power Energy Syst – volume: 12 start-page: 443 issue: 3 year: 1997 end-page: 452 article-title: Behavior‐mode simulation of power electronic circuits publication-title: IEEE Trans Power Electron – volume: 62 start-page: 1746 issue: 3 year: 2014 end-page: 1759 article-title: Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: state of the art publication-title: IEEE Trans Ind Electron – volume: 28 start-page: 591 issue: 1 year: 2012 end-page: 604 article-title: Survey on reliability of power electronic systems publication-title: IEEE Trans Power Electron – volume: 68 start-page: 10193 issue: 10 year: 2020 end-page: 10206 article-title: Switch open‐circuit fault localization strategy for MMCs using sliding‐time window based features extraction algorithm publication-title: IEEE Trans Ind Electron – volume: 35 start-page: 5190 issue: 5 year: 2019 end-page: 5204 article-title: Fault localization strategy for modular multilevel converters under submodule lower switch open‐circuit fault publication-title: IEEE Trans Power Electron – volume: 50 start-page: 252 year: 2017 end-page: 259 article-title: Research on WNN soft fault diagnosis for analog circuit based on adaptive UKF algorithm publication-title: Appl Soft Comput – volume: 38 start-page: 1731 issue: 3 year: 2022 end-page: 1746 article-title: Unsynchronized parameter‐free fault location for two or three terminal double‐circuit transmission lines sharing the same tower via unscented Kalman filter publication-title: IEEE Trans Power Delivery – volume: 65 start-page: 5224 issue: 7 year: 2017 end-page: 5236 article-title: Fault detection methods for three‐level NPC inverter based on DC‐bus electromagnetic signatures publication-title: IEEE Trans Ind Electron – volume: 48 start-page: 589 issue: 6‐7 year: 2020 end-page: 602 article-title: An automatic diagnosis of an inverter IGBT open‐circuit fault based on HHT‐ANN publication-title: Electr Pow Compo Sys – volume: 37 start-page: 4600 issue: 4 year: 2021 end-page: 4613 article-title: Submodule open‐circuit fault detection for modular multilevel converters under light load condition with rearranged bleeding resistor circuit publication-title: IEEE Trans Power Electron – volume: 176 year: 2023 article-title: An intelligent technique for fault detection and localization of three‐level ANPC inverter with NP connection for electric vehicles publication-title: Adv Eng Software – volume: 89 start-page: 171 year: 2016 end-page: 178 article-title: A sparse auto‐encoder‐based deep neural network approach for induction motor faults classification publication-title: Measurement – year: 2023 article-title: Machine learning for fault diagnosis of high‐speed train traction systems: a review publication-title: Front Eng Manag – ident: e_1_2_7_4_1 doi: 10.1109/TPEL.2009.2017082 – ident: e_1_2_7_32_1 doi: 10.1016/j.advengsoft.2022.103354 – ident: e_1_2_7_35_1 doi: 10.1016/j.ress.2022.108561 – ident: e_1_2_7_9_1 doi: 10.1016/j.ress.2023.109120 – ident: e_1_2_7_14_1 doi: 10.1007/s42524‐023‐0256‐2 – ident: e_1_2_7_30_1 doi: 10.1080/15325008.2020.1793835 – ident: e_1_2_7_12_1 doi: 10.1109/TPEL.2012.2192503 – ident: e_1_2_7_6_1 doi: 10.1016/j.rser.2017.03.021 – ident: e_1_2_7_19_1 doi: 10.1109/TPEL.2013.2242093 – ident: e_1_2_7_5_1 doi: 10.1002/qre.3172 – ident: e_1_2_7_33_1 doi: 10.1109/TIE.2018.2833045 – ident: e_1_2_7_18_1 doi: 10.1109/TPEL.2021.3122816 – ident: e_1_2_7_25_1 doi: 10.1109/TIE.2014.2348934 – ident: e_1_2_7_20_1 doi: 10.1016/j.ijepes.2019.105425 – ident: e_1_2_7_42_1 doi: 10.1016/j.asoc.2016.11.012 – ident: e_1_2_7_28_1 doi: 10.1109/TIE.2009.2013845 – year: 2015 ident: e_1_2_7_39_1 article-title: Multi‐scale context aggregation by dilated convolutions publication-title: arXiv preprint arXiv – ident: e_1_2_7_10_1 doi: 10.1002/qre.315 – ident: e_1_2_7_29_1 doi: 10.1109/TIE.2017.2777378 – ident: e_1_2_7_37_1 doi: 10.1121/1.395916 – ident: e_1_2_7_15_1 doi: 10.1109/TIE.2014.2375853 – ident: e_1_2_7_16_1 doi: 10.1109/TPEL.2016.2526684 – ident: e_1_2_7_34_1 doi: 10.1098/rspa.1998.0193 – ident: e_1_2_7_2_1 doi: 10.1109/5.4428 – ident: e_1_2_7_3_1 doi: 10.1109/63.575672 – year: 2012 ident: e_1_2_7_40_1 article-title: Improving neural networks by preventing co‐adaptation of feature detectors publication-title: arXiv preprint arXiv – ident: e_1_2_7_24_1 doi: 10.1109/TPEL.2014.2348194 – ident: e_1_2_7_26_1 doi: 10.1109/TPWRD.2022.3223410 – ident: e_1_2_7_27_1 doi: 10.1016/j.renene.2022.08.080 – ident: e_1_2_7_41_1 doi: 10.1109/5.931486 – ident: e_1_2_7_23_1 doi: 10.1109/TPEL.2019.2941129 – ident: e_1_2_7_38_1 doi: 10.1016/j.measurement.2016.04.007 – ident: e_1_2_7_13_1 doi: 10.1007/s42524-021-0169-x – ident: e_1_2_7_7_1 doi: 10.1002/qre.3319 – ident: e_1_2_7_22_1 doi: 10.1109/TIA.2021.3123199 – ident: e_1_2_7_36_1 doi: 10.1207/s15516709cog0901_7 – ident: e_1_2_7_17_1 doi: 10.1109/TPEL.2017.2679439 – ident: e_1_2_7_8_1 doi: 10.1002/qre.3280 – ident: e_1_2_7_21_1 doi: 10.1109/TIE.2022.3153810 – ident: e_1_2_7_11_1 doi: 10.1016/j.ymssp.2022.109628 – ident: e_1_2_7_31_1 doi: 10.1109/TIE.2020.3028808 |
| SSID | ssj0010098 |
| Score | 2.3620572 |
| Snippet | Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 819 |
| SubjectTerms | Algorithms Circuit design Circuits Control equipment Decomposition drive circuit Driver circuits Electronic components empirical mode decomposition (EMD) algorithm Fault diagnosis Fault location Faults Feature extraction Machine learning Nuclear reactors Phase current sparse convolutional autoencoder (SCAE) |
| Title | Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fqre.3452 https://www.proquest.com/docview/2922458983 |
| Volume | 40 |
| WOSCitedRecordID | wos001069740900001&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: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1099-1638 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0010098 issn: 0748-8017 databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ1NS8MwGMeDbB704Ls4nRJB9FTtkrRJjqIbHsbwbeDJ8jRJYSB1dp1nP4Kf0U9i0pdNQUHw1B6eQJs8L_-2T39B6EhQkwQguac7GjwmfelJQ6nHQ9UxwmgSJwXEtc8HA_HwIK-rrkr3L0zJh5i9cHORUeRrF-AQT87m0NCXzJxSFtj02yTWbVkDNS9ve8P-7BuCQ2WWEE7h8jCv0bM-OavHfi9Gc4X5VacWhaa3-p9LXEMrlbzE56U_rKMFk26g5S_QwU30eDe2D7MGu37zyu_sCJjmz45pqU328fbuapvGCUyfcuyqnTPCVt5indnsiNUoU9NRPsGjFKcOiAwZtuKz2LlnCw173fuLK6_aZcFTttQTzz7P2CAG32iI7YkOOkCZjH0JCjhLDOWxoomiVpqIIEgAtKONG24IQEBCoNuokT6nZgfhhLIYgCjX68niMBQiCayCY5ozo33gLXRST3ekKgS52wnjKSrhySSyMxa5GWuhw5nluMRu_GDTrlcsqgJvEhFpNUkgpKAtdFysza_jo5vbrjvu_tVwDy0Re2NlB1obNfJsavbRonrNR5PsoHK_T9QX4V0 |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS9xAEB_kLLQ-1NpWvPq1QrFPqbndTTZLn0Q9FM-jWgWfGia7GziQaHO5PvdP6N_oX9KdfJwKFgp9Sh5mIJmdj99uJr8B-JgIl0eoVWAHFgOpQx1oJ0SgYjNwibM8y2sS15Eaj5Pra_11Ab50_8I0_BDzAzeKjDpfU4DTgfTeA2voj9J9FjLy-XdRei-KerB4eDG8Gs0_IhBXZsPCmVAiVh33bMj3Ot2n1egBYj4GqnWlGS7_1zO-gdctwGT7jUeswIIr3sLSI9rBd_D9253fzjpGHeet53kNnFW3xGppXXn_6zdVN8tynN1UjOodCTEPcJktfX5kZlKa2aSasknBCqJExpJ5-FnP7nkPV8Ojy4PjoJ2zEBhf7HngdzQ-jDF0FjN_Y6MBCqmzUKNBJXMnVGZEbkRMVo5yREt84045jhjxGMUq9Irbwq0By4XMELmhbk-ZxXGS5JHHcNIq6WyIqg-fOnunpiUhp1kYN2lDn8xTb7GULNaHnbnkXUO88YzMRrdkaRt605Rrj0qiRCeiD7v14vxVPz2_OKLrh38V3IaXx5dno3R0Mj5dh1fcv2TTj7YBvaqcuU14YX5Wk2m51friH0zb5U0 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ1bSxwxFMcPolLaB7U3umptCqV9mjqbyyTBp1JdWros1lbwqcOZXGBBxu3srM9-hH7GfhKTuawWFAo-zTycwExyLv9MMr8AvFPMeYFaJnZoMeE61Yl2jCUyM0OnnKWFbyCuYzmZqLMzfbwCB_2_MC0fYvnBLUZGk69jgLuZ9fs31NDflfvIuAj5d40LnYWoXDs8GZ2Ol4sIkZXZUjhVTMSyZ8-mdL9v-281upGYt4VqU2lGmw96xi3Y6AQm-dR6xFNYceUzeHILO_gcfv2YhemsI3HHeed5oQUu6otItbSu-nv1J1Y3SzwuzmsS6100IkHgEluF_EjMtDKLaT0n05KUEYmMFQnyszm75wWcjo5-fv6SdOcsJCYUe5qEGU0IY0ydxSLcWDFExnWRajQouXdMFoZ5w4I4UUJ4RBt54046iihohuwlrJYXpXsFxDNeIFITd3vyIsuU8iJoOG4ldzZFOYAPfX_npoOQx7MwzvMWn0zz0GN57LEBvF1azlrwxh02u_2Q5V3ozXOqgyoRSis2gPfN4NzbPv9-chSv2_9r-AYeHR-O8vHXybcdeEzDO7bb0XZhta4W7jWsm8t6Oq_2Ole8BgDr5Mg |
| 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=Sparse+convolutional+autoencoder%E2%80%90based+fault+location+for+drive+circuits+in+nuclear+reactors&rft.jtitle=Quality+and+reliability+engineering+international&rft.au=Yang%2C+Cheng&rft.au=Yuan%2C+Yannan&rft.au=Wang%2C+Fu&rft.au=Li%2C+Jueying&rft.date=2024-03-01&rft.issn=0748-8017&rft.eissn=1099-1638&rft.volume=40&rft.issue=2&rft.spage=819&rft.epage=837&rft_id=info:doi/10.1002%2Fqre.3452&rft.externalDBID=10.1002%252Fqre.3452&rft.externalDocID=QRE3452 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0748-8017&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0748-8017&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0748-8017&client=summon |