Research on remaining service life prediction of platform screen doors system based on genetic algorithm to optimise BP neural network
The platform screen door system related to the safety of passengers, vehicle and station operation in rail transit is the research object. Relay, the key component of the PSD system, is selected as the breakthrough point, and the number of pull-in is used as the evaluation index of the service life...
Uložené v:
| Vydané v: | Enterprise information systems Ročník 16; číslo 8-9 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Taylor & Francis
03.08.2022
|
| Predmet: | |
| ISSN: | 1751-7575, 1751-7583 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The platform screen door system related to the safety of passengers, vehicle and station operation in rail transit is the research object. Relay, the key component of the PSD system, is selected as the breakthrough point, and the number of pull-in is used as the evaluation index of the service life of the relay. The precise and complex mapping between the influencing factors of the relay (coil resistance, pull-in voltage, release voltage, pull-in time, release time and contact resistance) and the remaining service life of the PSD system are established based on genetic algorithm to optimise BP neural network, and a new model for predicting the remaining life of the PSD is constructed. The PSD system of Guangzhou Metro Line 8 - KeCun Station is taken as the experimental test object. By comparing the BP algorithm, the PSO-BP algorithm and the GA-BP algorithm, it is demonstrated in detail how the prediction model can effectively predict the remaining service life of the PSD system with high precision, so that the equipment early warning before failure occurs to ensure the normal operation of subway lines. Experimental examples effectively prove the stability and accuracy of the GA-BP algorithm proposed. |
|---|---|
| AbstractList | The platform screen door system related to the safety of passengers, vehicle and station operation in rail transit is the research object. Relay, the key component of the PSD system, is selected as the breakthrough point, and the number of pull-in is used as the evaluation index of the service life of the relay. The precise and complex mapping between the influencing factors of the relay (coil resistance, pull-in voltage, release voltage, pull-in time, release time and contact resistance) and the remaining service life of the PSD system are established based on genetic algorithm to optimise BP neural network, and a new model for predicting the remaining life of the PSD is constructed. The PSD system of Guangzhou Metro Line 8 - KeCun Station is taken as the experimental test object. By comparing the BP algorithm, the PSO-BP algorithm and the GA-BP algorithm, it is demonstrated in detail how the prediction model can effectively predict the remaining service life of the PSD system with high precision, so that the equipment early warning before failure occurs to ensure the normal operation of subway lines. Experimental examples effectively prove the stability and accuracy of the GA-BP algorithm proposed. |
| Author | Liu, Qin Shi, Zihong Wei, Qianzhou Ling, Xiang Liu, Suping Zhang, Yu |
| Author_xml | – sequence: 1 givenname: Xiang surname: Ling fullname: Ling, Xiang organization: Guangdong Institute of Intelligent Manufacturing, Guangdong Key Laboratory of Modern Control Technology – sequence: 2 givenname: Suping surname: Liu fullname: Liu, Suping organization: College of Computer Science, Guangdong University of Science and Technology – sequence: 3 givenname: Qin surname: Liu fullname: Liu, Qin email: qinliu901@gmail.com organization: College of Automation, Zhongkai University of Agriculture and Engineering – sequence: 4 givenname: Qianzhou surname: Wei fullname: Wei, Qianzhou organization: Guangdong Institute of Intelligent Manufacturing, Guangdong Key Laboratory of Modern Control Technology – sequence: 5 givenname: Yu surname: Zhang fullname: Zhang, Yu organization: Guangdong Institute of Intelligent Manufacturing, Guangdong Key Laboratory of Modern Control Technology – sequence: 6 givenname: Zihong surname: Shi fullname: Shi, Zihong organization: Guangdong Polytechnic of Water Resources and Electric Engineering |
| BookMark | eNqFkN1KxDAQhYMoqKuPIOQFVpO2Sbp44w_-gaCIXpfZdLJG22SZRGVfwOe2xZ8LLxQGznCYc2C-bbYeYkDG9qTYl6IWB9KoYYzaL0QxWLWuKlOvsa3RnxpVl-s_u1GbbDulJyF0LYzaYu93mBDIPvIYOGEPPviw4Anp1VvknXfIl4Stt9kPF9HxZQfZRep5soQYeBsjJZ5WKWPP55CwHasWGDB7y6FbRPL5sec58rjMvvcJ-cktD_hC0A2S3yI977ANB13C3S-dsIfzs_vTy-n1zcXV6fH11JZS5CnUgOCMruZaalPNK-XkrJiZsihdURvTKqFq1KICrLRQuipmKMpSgJBipqUqJ0x99lqKKRG6Zkm-B1o1UjQjzOYbZjPCbL5gDrnDXznrM4xIMoHv_k0ffaZ9GMnB8HHXNhlWXSRHEKxPTfl3xQfUFJEZ |
| CitedBy_id | crossref_primary_10_20965_jaciii_2024_p1107 crossref_primary_10_3390_app15137188 crossref_primary_10_1080_10454446_2022_2028692 |
| Cites_doi | 10.1016/j.ins.2014.03.015 10.1109/TNNLS.2015.2500618 10.1016/j.asoc.2019.105733 10.1109/RAMS.2009.4914720 10.1016/j.neunet.2015.03.008 10.1016/j.ins.2016.07.067 10.1016/j.sjbs.2019.06.016 10.1177/1077546311435349 10.1177/1748006X18768916 10.1007/s11227-020-03171-8 10.1109/TCYB.2015.2490170 10.1016/j.asoc.2016.04.026 10.1080/17517575.2019.1701714 10.1016/j.ymssp.2008.06.009 10.1162/NECO_a_00549 10.1016/j.neunet.2014.10.003 10.1016/j.future.2017.12.005 10.1016/j.neunet.2014.05.014 10.1016/j.buildenv.2004.01.022 10.1007/s00500-014-1371-0 10.1007/s00500-014-1329-2 10.1016/j.ymssp.2005.11.008 10.1007/s12652-019-01624-4 10.1109/TNNLS.2015.2441697 10.1016/j.engappai.2018.05.003 10.1016/j.eswa.2019.05.030 10.1016/j.neucom.2017.11.062 |
| ContentType | Journal Article |
| Copyright | 2021 Informa UK Limited, trading as Taylor & Francis Group 2021 |
| Copyright_xml | – notice: 2021 Informa UK Limited, trading as Taylor & Francis Group 2021 |
| DBID | AAYXX CITATION |
| DOI | 10.1080/17517575.2020.1864478 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1751-7583 |
| ExternalDocumentID | 10_1080_17517575_2020_1864478 1864478 |
| Genre | Research Article |
| GroupedDBID | .7F 0BK 0R~ 30N 4.4 8VB AAGDL AAHIA AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABFIM ABJNI ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACGFS ACTIO ADCVX ADGTB ADMLS AEISY AENEX AEYOC AFRVT AGDLA AHDZW AHQJS AIJEM AIYEW AKBVH AKOOK AKVCP ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AQTUD AVBZW AWYRJ BLEHA CCCUG CE4 CS3 DGEBU DKSSO DU5 EBS EBU E~A E~B GTTXZ H13 HZ~ H~P IPNFZ J~4 K1G KYCEM LJTGL M4Z MK~ O9- P2P QWB RIG RNANH ROSJB RTWRZ S-T SNACF TASJS TBQAZ TDBHL TEN TFL TFT TFW TH9 TTHFI TUROJ TWF TWQ UT5 UU3 ZGOLN ZL0 AAYXX CITATION |
| ID | FETCH-LOGICAL-c310t-a8aeaf764b61674b45f19297323f2877d5058e604ae46056429e0330a01096153 |
| IEDL.DBID | TFW |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000613768500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-7575 |
| IngestDate | Tue Nov 18 21:09:44 EST 2025 Sat Nov 29 06:46:09 EST 2025 Mon Oct 20 23:47:10 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8-9 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c310t-a8aeaf764b61674b45f19297323f2877d5058e604ae46056429e0330a01096153 |
| ParticipantIDs | crossref_citationtrail_10_1080_17517575_2020_1864478 informaworld_taylorfrancis_310_1080_17517575_2020_1864478 crossref_primary_10_1080_17517575_2020_1864478 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-08-03 |
| PublicationDateYYYYMMDD | 2022-08-03 |
| PublicationDate_xml | – month: 08 year: 2022 text: 2022-08-03 day: 03 |
| PublicationDecade | 2020 |
| PublicationTitle | Enterprise information systems |
| PublicationYear | 2022 |
| Publisher | Taylor & Francis |
| Publisher_xml | – name: Taylor & Francis |
| References | cit0011 Li X. C. (cit0012) 2019 cit0031 cit0010 cit0032 cit0030 Hu Y. T. (cit0007) 2011 Xu Y. S. (cit0028) 2019 cit0019 Xiong L. (cit0026) 2019 cit0017 Zhou L. (cit0041) 2009 Wei Q. Z. (cit0018) 2017 cit0015 cit0037 cit0038 cit0035 Zhang Y. B. (cit0039) 2012 cit0014 cit0036 cit0022 cit0001 cit0023 (cit0033) 2013; 222 cit0021 Yousef (cit0034) 2019 cit0040 Liu G. (cit0013) 2015; 61 Wu Y. (cit0020) 2016 Yana Z. (cit0029) 2019 Cai P. F. (cit0003) 2019 cit0008 cit0009 cit0006 cit0004 cit0005 Su H. (cit0016) 2013 cit0027 cit0002 cit0024 cit0025 |
| References_xml | – ident: cit0022 doi: 10.1016/j.ins.2014.03.015 – ident: cit0024 doi: 10.1109/TNNLS.2015.2500618 – ident: cit0005 doi: 10.1016/j.asoc.2019.105733 – ident: cit0017 doi: 10.1109/RAMS.2009.4914720 – year: 2019 ident: cit0003 publication-title: Computer Measurement & Control – year: 2016 ident: cit0020 publication-title: Bei Jing Jiao Tong University – year: 2011 ident: cit0007 publication-title: Journal of Shanghai Jiaotong University – year: 2019 ident: cit0028 publication-title: Huazhong University of Science and Technology – ident: cit0023 doi: 10.1016/j.neunet.2015.03.008 – ident: cit0030 doi: 10.1016/j.ins.2016.07.067 – year: 2009 ident: cit0041 publication-title: Building and Environment – year: 2019 ident: cit0029 publication-title: Energy Conversion and Management – year: 2013 ident: cit0016 publication-title: South China University of Technology – year: 2019 ident: cit0034 publication-title: Measurement – ident: cit0004 doi: 10.1016/j.sjbs.2019.06.016 – year: 2019 ident: cit0012 publication-title: Applied Soft Computing Journal – ident: cit0031 doi: 10.1177/1077546311435349 – ident: cit0002 doi: 10.1177/1748006X18768916 – ident: cit0027 doi: 10.1007/s11227-020-03171-8 – ident: cit0037 doi: 10.1109/TCYB.2015.2490170 – ident: cit0010 doi: 10.1016/j.asoc.2016.04.026 – ident: cit0035 doi: 10.1080/17517575.2019.1701714 – ident: cit0006 doi: 10.1016/j.ymssp.2008.06.009 – ident: cit0021 doi: 10.1162/NECO_a_00549 – year: 2012 ident: cit0039 publication-title: Journal of Vibration and Shock – volume: 61 start-page: 59 year: 2015 ident: cit0013 publication-title: IEEE Transactions on Cybernetics – ident: cit0038 doi: 10.1016/j.neunet.2014.10.003 – ident: cit0011 doi: 10.1016/j.future.2017.12.005 – ident: cit0040 doi: 10.1016/j.neunet.2014.05.014 – year: 2017 ident: cit0018 publication-title: China Science and Technology Information – ident: cit0019 doi: 10.1016/j.buildenv.2004.01.022 – ident: cit0032 doi: 10.1007/s00500-014-1371-0 – ident: cit0014 doi: 10.1007/s00500-014-1329-2 – volume: 222 start-page: 413 issue: 3 year: 2013 ident: cit0033 publication-title: Information Sciences – ident: cit0008 doi: 10.1016/j.ymssp.2005.11.008 – ident: cit0025 doi: 10.1007/s12652-019-01624-4 – year: 2019 ident: cit0026 publication-title: Soft Computing – ident: cit0036 doi: 10.1109/TNNLS.2015.2441697 – ident: cit0001 doi: 10.1016/j.engappai.2018.05.003 – ident: cit0009 doi: 10.1016/j.eswa.2019.05.030 – ident: cit0015 doi: 10.1016/j.neucom.2017.11.062 |
| SSID | ssj0068075 |
| Score | 2.3022614 |
| Snippet | The platform screen door system related to the safety of passengers, vehicle and station operation in rail transit is the research object. Relay, the key... |
| SourceID | crossref informaworld |
| SourceType | Enrichment Source Index Database Publisher |
| SubjectTerms | ga-BP Neural Network optimisation Platform Screen Doors System prediction remaining Service Life |
| Title | Research on remaining service life prediction of platform screen doors system based on genetic algorithm to optimise BP neural network |
| URI | https://www.tandfonline.com/doi/abs/10.1080/17517575.2020.1864478 |
| Volume | 16 |
| WOSCitedRecordID | wos000613768500001&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: PRVAWR databaseName: Taylor & Francis Online Journals customDbUrl: eissn: 1751-7583 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0068075 issn: 1751-7575 databaseCode: TFW dateStart: 20070201 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELZQxQADb0R56QbWQBq7cTIComJAVYcC3SI7saFSm1RJ4Cfwu7nLA7UDMICUIYp0lmWf7-6L775j7EIKa32puSNDhKtCSO0obbWjXdvjAb6qqp3P04McDoPJJBw12YRFk1ZJGNrWRBGVrabDrXTRZsRdocfDR_YR3Xn4KUCXLqncF10_Hc3x4Lm1xX5DtUsSDom0NTzfjbLinVa4S5e8zmD7H-a7w7aakBOuax3ZZWsm3WObS0SE--yjTcCDLIXczOu2EVDUhgRmU2tgkdOdDu0jZBYWM1XStAHNDkJhSLIsL6DmhQZyjQkNhdpJRZKgZi9ZPi1f51BmkKGVQu0ycDMCotPEuaV1MvoBexzcjW_vnaZDgxNjWFg6KlBGWekL7VM1gxZ9ixEjEQBxi1BMJhhfBcZ3hTJ0_4pYJzQu566iCzkKNQ9ZJ81Sc8TASzgXfU9zJTCoM0q7AWI3K1XMVexbr8tEuzNR3NCXUxeNWdRrWE7bZY5omaNmmbvs8ktsUfN3_CYQLm97VFY_Tmzd5STiP8oe_0H2hG14VFlB2Sj8lHXK_M2csfX4vZwW-Xml1Z-T7vM4 |
| linkProvider | Taylor & Francis |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT8MwDI5gIAEH3ojx9IFroWuypjsCYhpiTBwG7FYlXQKTtnbqCj-B3429tmg7AAeQeqgqOYoS1_aX2J8ZO5PCWl9q7sgGwlUhpHaUttrRrq3xAF_VtJ3PU1t2OkGv15ithaG0SsLQNieKmNpq-rnpMLpMibtAl4ePrCO88_BTgD5dBotsqY6-lvjzu83n0hr7BdkuiTgkU1bxfDfMnH-aYy-d8TvNjf-Y8SZbL6JOuMzVZIstmHibrc1wEe6wjzIHD5IYUjPKO0fAJLclMBxYA-OUrnVoKyGxMB6qjOYNaHkQDUM_SdIJ5NTQQN6xT0OhglKdJKjhS5IOstcRZAkkaKhQwQxcPQAxauLc4jwffZc9Nm-61y2naNLgRBgZZo4KlFFW-kL7VNCgRd1i0EgcQNwiGpN9DLEC47tCGbqCRbjTMC7nrqI7OYo291glTmKzz8Drcy7qnuZKYFxnlHYDhG9WqoiryLdelYlya8KoYDCnRhrDsFYQnZbLHNIyh8UyV9n5l9g4p_D4TaAxu-9hNj07sXmjk5D_KHvwB9lTttLq3rfD9m3n7pCtelRoQckp_IhVsvTNHLPl6D0bTNKTqYp_AgZJ92I |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT8MwDI5gIAQH3ojx9IFroTRZ0x15TSCmaYcBu1VJm8Ck0U5d4Sfwu7HXFm0H4ABSD1UlR1Hi2P4a-zNjJ1JY60vNHdlEuCqE1I7SVjvatec8wFc1aefz2JadTtDvN7tlNuG4TKskDG0LooiJrabDPYptlRF3hh4PH9lAdOfhpwBdugzm2QKGzj4pea_1VBljv-TaJRGHZKoinu-GmXFPM-SlU26ntfYPE15nq2XMCReFkmywOZNsspUpJsIt9lFl4EGaQGZei74RMC4sCQwH1sAoo0sd2khILYyGKqdpA9odxMIQp2k2hoIYGsg3xjQUqidVSYIaPqfZIH95hTyFFM0UqpeByy4QnybOLSmy0bfZQ-umd3XrlC0anAjjwtxRgTLKSl9on8oZtGhYDBmJAYhbxGIyxgArML4rlKELWAQ7TeNy7iq6kaNYc4fVkjQxuwy8mHPR8DRXAqM6o7QbIHizUkVcRb716kxUOxNGJX85tdEYhuclzWm1zCEtc1guc52dfomNCgKP3wSa09se5pM_J7ZocxLyH2X3_iB7zJa6162wfde532fLHlVZUGYKP2C1PHszh2wxes8H4-xoouCfGz32FA |
| 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=Research+on+remaining+service+life+prediction+of+platform+screen+doors+system+based+on+genetic+algorithm+to+optimise+BP+neural+network&rft.jtitle=Enterprise+information+systems&rft.au=Ling%2C+Xiang&rft.au=Liu%2C+Suping&rft.au=Liu%2C+Qin&rft.au=Wei%2C+Qianzhou&rft.date=2022-08-03&rft.pub=Taylor+%26+Francis&rft.issn=1751-7575&rft.eissn=1751-7583&rft.volume=16&rft.issue=8-9&rft_id=info:doi/10.1080%2F17517575.2020.1864478&rft.externalDocID=1864478 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-7575&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-7575&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-7575&client=summon |