Reliability study of generalized exponential distribution based on inverse power law using artificial neural network with Bayesian regularization
The investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical investigators have become more interested in lifetime model exploration in recent years, particularly in the last decade, without considering...
Saved in:
| Published in: | Quality and reliability engineering international Vol. 39; no. 6; pp. 2398 - 2421 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Bognor Regis
Wiley Subscription Services, Inc
01.10.2023
|
| Subjects: | |
| ISSN: | 0748-8017, 1099-1638 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical investigators have become more interested in lifetime model exploration in recent years, particularly in the last decade, without considering the issue of modeling the metrics of model reliability using artificial neural networks (ANNs). This study addresses this vacuum by discussing the multilayer ANN with Bayesian regularization modeling for reliability metrics of generalized exponential model based on inverse power law (IPL). The numerical findings of the reliability investigations and the values obtained from the ANN have been examined and analyzed carefully. The findings show that ANNs are a powerful and useful mathematical tool for analyzing the reliability of lifetime model based on IPL. Finally, a real life framework is implemented that support the theory of a research study. |
|---|---|
| AbstractList | The investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical investigators have become more interested in lifetime model exploration in recent years, particularly in the last decade, without considering the issue of modeling the metrics of model reliability using artificial neural networks (ANNs). This study addresses this vacuum by discussing the multilayer ANN with Bayesian regularization modeling for reliability metrics of generalized exponential model based on inverse power law (IPL). The numerical findings of the reliability investigations and the values obtained from the ANN have been examined and analyzed carefully. The findings show that ANNs are a powerful and useful mathematical tool for analyzing the reliability of lifetime model based on IPL. Finally, a real life framework is implemented that support the theory of a research study. The investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical investigators have become more interested in lifetime model exploration in recent years, particularly in the last decade, without considering the issue of modeling the metrics of model reliability using artificial neural networks (ANNs). This study addresses this vacuum by discussing the multilayer ANN with Bayesian regularization modeling for reliability metrics of generalized exponential model based on inverse power law (IPL). The numerical findings of the reliability investigations and the values obtained from the ANN have been examined and analyzed carefully. The findings show that ANNs are a powerful and useful mathematical tool for analyzing the reliability of lifetime model based on IPL. Finally, a real life framework is implemented that support the theory of a research study. |
| Author | Çolak, Andaç Batur Sindhu, Tabassum Naz Lone, Showkat Ahmad Shafiq, Anum |
| Author_xml | – sequence: 1 givenname: Tabassum Naz orcidid: 0000-0001-9433-4981 surname: Sindhu fullname: Sindhu, Tabassum Naz organization: Department of Statistics Quaid‐i‐Azam University Islamabad Pakistan – sequence: 2 givenname: Andaç Batur orcidid: 0000-0001-9297-8134 surname: Çolak fullname: Çolak, Andaç Batur organization: Information Technologies Application and Research Center Istanbul Ticaret University Istanbul Türkiye – sequence: 3 givenname: Showkat Ahmad surname: Lone fullname: Lone, Showkat Ahmad organization: Department of Basic Sciences College of Science and Theoretical Studies Saudi Electronic University Riyadh Kingdom of Saudi Arabia – sequence: 4 givenname: Anum orcidid: 0000-0001-7186-7216 surname: Shafiq fullname: Shafiq, Anum organization: School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing China |
| BookMark | eNplUMtKxDAUDaLg-AA_IeDGTcckbfpY6uALBgTRdUnTZLxjTWduUsfxL_xjU3Wlq3PgnsflHJBd1ztDyAlnU86YOF-jmaapFDtkwllVJTxPy10yYUVWJiXjxT458H7JWBRX5YR8PpgOVAMdhC31YWi3tLd0YZxB1cGHaal5X8UGF0B1tAUfEJohQO9oo3w8RwLuzaA3dNVvDNJObejgwS2owgAW9Gh0ZsBvCJseX-gGwjO9VFvjQTmKZjF0CuFDjblHZM-qzpvjXzwkT9dXj7PbZH5_cze7mCdaSBmSVjaa8VaKNsslz0VRtVaJVjRKlpHK3BY6i1dVNkw3QmeVkJVttLCiEKlN00Ny-pO7wn49GB_qZT-gi5W1KHOelZVko2r6o9LYe4_G1hrC958BFXQ1Z_U4ex1nr8fZo-Hsj2GF8Kpw-1_6BdAjicc |
| CitedBy_id | crossref_primary_10_1002_qre_3512 crossref_primary_10_1002_qre_3633 crossref_primary_10_1016_j_jrras_2024_100879 crossref_primary_10_1016_j_heliyon_2024_e28609 crossref_primary_10_3389_fenrg_2024_1335642 crossref_primary_10_1016_j_triboint_2023_109231 crossref_primary_10_1016_j_applthermaleng_2024_122757 crossref_primary_10_1016_j_cnsns_2025_108733 crossref_primary_10_1002_zamm_202400208 crossref_primary_10_1016_j_rineng_2024_103420 crossref_primary_10_1016_j_rineng_2025_107204 crossref_primary_10_1007_s44198_024_00196_y crossref_primary_10_1016_j_rineng_2024_103233 crossref_primary_10_1016_j_applthermaleng_2025_128450 |
| Cites_doi | 10.1016/j.matdes.2018.05.049 10.1109/TIE.2009.2014306 10.1016/j.icheatmasstransfer.2016.03.008 10.1002/er.5680 10.3390/math8020160 10.1016/j.oceaneng.2021.108793 10.1081/STA-100107697 10.1016/j.jece.2020.104711 10.1002/fld.5038 10.1109/ACCESS.2020.3000951 10.1016/j.spmi.2021.106864 10.1016/j.neucom.2020.03.051 10.1016/j.rinp.2022.105613 10.1016/j.asoc.2021.107551 10.1016/j.jrmge.2019.04.006 10.1016/j.petrol.2015.01.002 10.1002/er.5417 10.1016/j.cjph.2022.04.004 10.1615/HeatTransRes.2020034724 10.1016/j.powtec.2019.05.034 10.1017/S0269964801151077 10.1016/j.jmaa.2009.08.029 10.1017/S0269964800005064 10.1137/0108051 10.1016/j.apacoust.2018.04.020 10.1002/qre.2214 10.1016/j.icheatmasstransfer.2017.02.003 10.1080/03610920902807895 10.1016/j.cma.2021.113976 10.1109/TR.2008.2006576 10.1002/mma.8178 10.3390/electronics8121412 10.1016/j.petrol.2011.03.002 10.1111/1467-842X.00072 10.1016/j.jspi.2007.03.030 10.1109/TR.2013.2255793 10.1016/j.jhydrol.2017.11.049 10.2307/3556181 10.1155/2019/8947905 10.1002/qre.2864 10.1016/j.powtec.2014.06.062 10.1615/HeatTransRes.2021041018 |
| ContentType | Journal Article |
| Copyright | 2023 John Wiley & Sons Ltd. |
| Copyright_xml | – notice: 2023 John Wiley & Sons Ltd. |
| DBID | AAYXX CITATION 7TB 8FD FR3 |
| DOI | 10.1002/qre.3352 |
| 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 | 2421 |
| ExternalDocumentID | 10_1002_qre_3352 |
| 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 AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAYXX AAZKR ABCQN ABCUV ABEML ABIJN ABJNI ABPVW ACAHQ ACBWZ ACCZN ACGFS ACIWK ACPOU ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADMGS ADMLS ADNMO ADOZA ADXAS AEFGJ AEIGN AEIMD AENEX AEUYR AEYWJ AFBPY AFFNX AFFPM AFGKR AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AIDQK AIDYY AIQQE AITYG AIURR AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALVPJ AMBMR AMYDB ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CITATION CMOOK CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EBS EJD 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 O8X O9- P2P P2W P2X P4D PALCI Q.N Q11 QB0 QRW R.K RIWAO RJQFR RNS ROL RX1 RYL SAMSI SUPJJ TN5 UB1 V2E W8V W99 WBKPD WH7 WIH WIK WLBEL WOHZO WQJ WXSBR WYISQ XG1 XPP XV2 ZZTAW ~IA ~WT 7TB 8FD ALUQN FR3 |
| ID | FETCH-LOGICAL-c255t-d5bc01d52d46516279dfa2d2ba589df56f7c452da8b0cb2c49259fbc2f2723f33 |
| ISICitedReferencesCount | 23 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000972508300001&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 | Fri Jul 25 12:13:12 EDT 2025 Sat Nov 29 02:33:18 EST 2025 Tue Nov 18 22:11:21 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c255t-d5bc01d52d46516279dfa2d2ba589df56f7c452da8b0cb2c49259fbc2f2723f33 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9297-8134 0000-0001-9433-4981 0000-0001-7186-7216 |
| PQID | 2861489503 |
| PQPubID | 1016437 |
| PageCount | 24 |
| ParticipantIDs | proquest_journals_2861489503 crossref_citationtrail_10_1002_qre_3352 crossref_primary_10_1002_qre_3352 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-10-00 20231001 |
| PublicationDateYYYYMMDD | 2023-10-01 |
| PublicationDate_xml | – month: 10 year: 2023 text: 2023-10-00 |
| PublicationDecade | 2020 |
| PublicationPlace | Bognor Regis |
| PublicationPlace_xml | – name: Bognor Regis |
| PublicationTitle | Quality and reliability engineering international |
| PublicationYear | 2023 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | Lyu MR (e_1_2_9_24_1) 1996 e_1_2_9_52_1 e_1_2_9_50_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_12_1 e_1_2_9_33_1 Ranjbar AH (e_1_2_9_34_1) 2009 Lawless JF (e_1_2_9_2_1) 1982 e_1_2_9_14_1 e_1_2_9_39_1 Meeker WQ (e_1_2_9_4_1) 2014 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_20_1 Zhang L (e_1_2_9_8_1) 2019; 2019 e_1_2_9_22_1 e_1_2_9_45_1 Bandemer H (e_1_2_9_5_1) 1978 e_1_2_9_43_1 e_1_2_9_6_1 Lee YK (e_1_2_9_30_1) 2008; 21 e_1_2_9_26_1 e_1_2_9_49_1 e_1_2_9_28_1 e_1_2_9_47_1 Goel A (e_1_2_9_31_1) 2007 e_1_2_9_51_1 e_1_2_9_11_1 e_1_2_9_13_1 e_1_2_9_32_1 Huang X (e_1_2_9_7_1) 2014; 228 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_19_1 Martz HF (e_1_2_9_3_1) 1982 e_1_2_9_42_1 e_1_2_9_40_1 e_1_2_9_21_1 e_1_2_9_46_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_9_1 e_1_2_9_25_1 e_1_2_9_27_1 e_1_2_9_48_1 e_1_2_9_29_1 |
| References_xml | – ident: e_1_2_9_17_1 doi: 10.1016/j.matdes.2018.05.049 – ident: e_1_2_9_33_1 doi: 10.1109/TIE.2009.2014306 – ident: e_1_2_9_42_1 doi: 10.1016/j.icheatmasstransfer.2016.03.008 – ident: e_1_2_9_43_1 doi: 10.1002/er.5680 – ident: e_1_2_9_9_1 doi: 10.3390/math8020160 – ident: e_1_2_9_45_1 doi: 10.1016/j.oceaneng.2021.108793 – ident: e_1_2_9_28_1 doi: 10.1081/STA-100107697 – ident: e_1_2_9_20_1 doi: 10.1016/j.jece.2020.104711 – ident: e_1_2_9_50_1 doi: 10.1002/fld.5038 – ident: e_1_2_9_11_1 doi: 10.1109/ACCESS.2020.3000951 – ident: e_1_2_9_41_1 doi: 10.1016/j.spmi.2021.106864 – ident: e_1_2_9_23_1 doi: 10.1016/j.neucom.2020.03.051 – ident: e_1_2_9_48_1 doi: 10.1016/j.rinp.2022.105613 – ident: e_1_2_9_21_1 doi: 10.1016/j.asoc.2021.107551 – start-page: 253 volume-title: Compatibility and Power Electronics year: 2009 ident: e_1_2_9_34_1 – ident: e_1_2_9_47_1 doi: 10.1016/j.jrmge.2019.04.006 – ident: e_1_2_9_22_1 doi: 10.1016/j.petrol.2015.01.002 – ident: e_1_2_9_49_1 doi: 10.1002/er.5417 – volume-title: Handbook of Software Reliability Engineering year: 1996 ident: e_1_2_9_24_1 – ident: e_1_2_9_44_1 doi: 10.1016/j.cjph.2022.04.004 – volume: 228 start-page: 72 issue: 1 year: 2014 ident: e_1_2_9_7_1 article-title: A probability estimation method for reliability analysis using mapped Gegenbauer polynomials publication-title: Proc Inst Mech Eng O: J Risk Reliab – ident: e_1_2_9_39_1 doi: 10.1615/HeatTransRes.2020034724 – ident: e_1_2_9_16_1 doi: 10.1016/j.powtec.2019.05.034 – ident: e_1_2_9_27_1 doi: 10.1017/S0269964801151077 – ident: e_1_2_9_29_1 doi: 10.1016/j.jmaa.2009.08.029 – ident: e_1_2_9_26_1 doi: 10.1017/S0269964800005064 – ident: e_1_2_9_32_1 doi: 10.1137/0108051 – ident: e_1_2_9_19_1 doi: 10.1016/j.apacoust.2018.04.020 – volume-title: Statistical Models and Methods for Lifetime Data year: 1982 ident: e_1_2_9_2_1 – ident: e_1_2_9_6_1 doi: 10.1002/qre.2214 – ident: e_1_2_9_15_1 doi: 10.1016/j.icheatmasstransfer.2017.02.003 – ident: e_1_2_9_35_1 doi: 10.1080/03610920902807895 – ident: e_1_2_9_18_1 doi: 10.1016/j.cma.2021.113976 – ident: e_1_2_9_36_1 doi: 10.1109/TR.2008.2006576 – ident: e_1_2_9_52_1 doi: 10.1002/mma.8178 – ident: e_1_2_9_10_1 doi: 10.3390/electronics8121412 – volume-title: Statistical Methods for Reliability Data year: 2014 ident: e_1_2_9_4_1 – volume-title: Statistical Analysis of Reliability and Life‐Testing Models: Theory and Methods. Statistics: Textbooks and Monographs year: 1978 ident: e_1_2_9_5_1 – volume-title: A New Approach to Electronic Systems Reliability Assessment year: 2007 ident: e_1_2_9_31_1 – ident: e_1_2_9_40_1 doi: 10.1016/j.petrol.2011.03.002 – ident: e_1_2_9_13_1 doi: 10.1111/1467-842X.00072 – start-page: 10158 volume-title: Bayesian Reliability Analysis year: 1982 ident: e_1_2_9_3_1 – ident: e_1_2_9_14_1 doi: 10.1016/j.jspi.2007.03.030 – ident: e_1_2_9_37_1 doi: 10.1109/TR.2013.2255793 – ident: e_1_2_9_46_1 doi: 10.1016/j.jhydrol.2017.11.049 – ident: e_1_2_9_25_1 doi: 10.2307/3556181 – volume: 2019 year: 2019 ident: e_1_2_9_8_1 article-title: Reliability assessment for very few failure data and Weibull distribution publication-title: Math Probl Eng doi: 10.1155/2019/8947905 – volume: 21 start-page: 573 issue: 4 year: 2008 ident: e_1_2_9_30_1 article-title: A study on the techniques of estimating the probability of failure publication-title: J Chungcheong Math Soc – ident: e_1_2_9_12_1 doi: 10.1002/qre.2864 – ident: e_1_2_9_38_1 doi: 10.1016/j.powtec.2014.06.062 – ident: e_1_2_9_51_1 doi: 10.1615/HeatTransRes.2021041018 |
| SSID | ssj0010098 |
| Score | 2.4473422 |
| Snippet | The investigation of lifetime reliability analysis is vital for confirming the quality of devices, equipment, electronic tube flops, and so forth. Statistical... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 2398 |
| SubjectTerms | Artificial neural networks Bayesian analysis Mathematical analysis Multilayers Power law Probability distribution functions Regularization Reliability analysis |
| Title | Reliability study of generalized exponential distribution based on inverse power law using artificial neural network with Bayesian regularization |
| URI | https://www.proquest.com/docview/2861489503 |
| Volume | 39 |
| WOSCitedRecordID | wos000972508300001&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/eLvHCXMwtV1Lb9NAEF61KQc4VDxFoaBFQnCwDO76sfYxQCsOUUEkRblZtneXRAQncZM27b_gHzOzu35EqlA5cHE2thOvPZ9nZndnviHktefnESsC6eYeK9ygUNCSUe5KFbEE1KEodJWI7wN-ehqPx8nXnd39OhfmYsbLMt5sksV_FTXsA2Fj6uw_iLv5U9gBbRA6bEHssL2V4DHI2JBvXxnyWPQHfxh26ek1-Jdys5iXGCRk1meaklcOWjTh6NhHDNaQzgJLqDmz7NJZ6ykFvJplnEAeTP2ho8jNdO6H7ErqnMxKF7ivbIpn1_81lB2G86nqdFS2tIiawaKZpGxnFqB38BjBHFw7w2kpJut2BkNker2fQw_Agjr4JeYwZm-ykIaT-eXPbOX0J78y4QzmbSxBHzMBhpNMTZfdCRDWhtLdVs12VCoPYrTJxsRLo_KRoxS90q5NMARLFvtbCt43RbOts4AL6jcaIkNsu6zkO0xqa41tHWBw-iU9ORsM0tHxePRmsXSxDBqGC9iaMLtkj_EwiXtk79M3OLFZGEP-V8Msa26k5lP22Pv6Ytse1raDob2m0X2yb4c7tG9g-oDsyPIhudchwXxEfncASzVg6VzRDmBpB7C0C1iqAUuhYQFLNWApAJZqwNIWsNQAllrAUgQsrQFLtwH7mJydHI8-fnZtoRC3gBHxyhVhXnhHImQiiMKjiPFEqIwJlmdhDM0wUrwI4GgW516Rg2pKYNCv8oIpxpmvfP8J6ZVwI08JVYHngxULslglQSwU_CAAD0BxmQgYifAD8rZ-uGlhWfSxmMssNfzfLAUxpCiGA_KqOXNhmGNuOOewlk9qVch5ymIk501Cz3_298PPyd32fTgkvVW1li_IneJiNT2vXlrk_AHpqNHv |
| linkProvider | Wiley-Blackwell |
| 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=Reliability+study+of+generalized+exponential+distribution+based+on+inverse+power+law+using+artificial+neural+network+with+Bayesian+regularization&rft.jtitle=Quality+and+reliability+engineering+international&rft.au=Tabassum+Naz+Sindhu&rft.au=Anda%C3%A7+Batur+%C3%87olak&rft.au=Showkat+Ahmad+Lone&rft.au=Anum+Shafiq&rft.date=2023-10-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=0748-8017&rft.eissn=1099-1638&rft.volume=39&rft.issue=6&rft.spage=2398&rft.epage=2421&rft_id=info:doi/10.1002%2Fqre.3352&rft.externalDBID=NO_FULL_TEXT |
| 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 |