Deeppipe: A semi-supervised learning for operating condition recognition of multi-product pipelines
Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure pipeline safe, reducing potential risk. However, the operating conditions of the multi-product pipeline change frequently, and the recognition and monitoring by on-site personnel are easy to cause misjudgment,...
Uloženo v:
| Vydáno v: | Process safety and environmental protection Ročník 150; s. 510 - 521 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Rugby
Elsevier B.V
01.06.2021
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0957-5820, 1744-3598 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure pipeline safe, reducing potential risk. However, the operating conditions of the multi-product pipeline change frequently, and the recognition and monitoring by on-site personnel are easy to cause misjudgment, so the operating conditions of the pipeline cannot be accurately recognized. Noticeably, operating condition recognition is an important part of pipeline safety and risk management. Although ample operating data are stored in SCADA system, these data are lack of corresponding condition labels, making it hard to be mined. In this work, a semi-supervised learning for operating condition recognition is proposed to overcome aforementioned issues. Firstly, the operating parameters of each station are preprocessed and collected to construct into data matrices to overcome transient disturbance considering the pipeline space characteristics and time series of the operating data. Then stacked autoencoder (SAE) is used to pre-train the network parameters of multi-layer neural network (MLNN) based on a large amount of unlabeled operating data. After that, MLNN is fine-tuned based on a small amount of labeled data annotated by referring to the operation log. To verify the effectiveness of the semi-supervised learning, a real multi-product pipeline is taken as an example for operating condition recognition. The accuracy, precision, recall and F1 score is 95 %, 95 %, 80 % and 80 %, respectively. Results show that the condition recognition accuracy of the proposed model is better than other machine learning models. Finally, the sensitivity analysis is conducted to illustrate the importance of SAE in this classification model. |
|---|---|
| AbstractList | Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure pipeline safe, reducing potential risk. However, the operating conditions of the multi-product pipeline change frequently, and the recognition and monitoring by on-site personnel are easy to cause misjudgment, so the operating conditions of the pipeline cannot be accurately recognized. Noticeably, operating condition recognition is an important part of pipeline safety and risk management. Although ample operating data are stored in SCADA system, these data are lack of corresponding condition labels, making it hard to be mined. In this work, a semi-supervised learning for operating condition recognition is proposed to overcome aforementioned issues. Firstly, the operating parameters of each station are preprocessed and collected to construct into data matrices to overcome transient disturbance considering the pipeline space characteristics and time series of the operating data. Then stacked autoencoder (SAE) is used to pre-train the network parameters of multi-layer neural network (MLNN) based on a large amount of unlabeled operating data. After that, MLNN is fine-tuned based on a small amount of labeled data annotated by referring to the operation log. To verify the effectiveness of the semi-supervised learning, a real multi-product pipeline is taken as an example for operating condition recognition. The accuracy, precision, recall and F1 score is 95 %, 95 %, 80 % and 80 %, respectively. Results show that the condition recognition accuracy of the proposed model is better than other machine learning models. Finally, the sensitivity analysis is conducted to illustrate the importance of SAE in this classification model. |
| Author | Li, Zhengbing Zhang, Haoran Du, Jian Wu, Yi Liao, Qi Liang, Yongtu Zheng, Jianqin |
| Author_xml | – sequence: 1 givenname: Jianqin surname: Zheng fullname: Zheng, Jianqin email: zhengjianqin_cup@163.com organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 2 givenname: Jian surname: Du fullname: Du, Jian organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 3 givenname: Yongtu surname: Liang fullname: Liang, Yongtu email: liangyt21st@163.com organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 4 givenname: Qi orcidid: 0000-0003-3209-9992 surname: Liao fullname: Liao, Qi email: qliao_cup@outlook.com organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 5 givenname: Zhengbing surname: Li fullname: Li, Zhengbing organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 6 givenname: Haoran surname: Zhang fullname: Zhang, Haoran organization: Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan – sequence: 7 givenname: Yi orcidid: 0000-0002-9867-6898 surname: Wu fullname: Wu, Yi organization: University of Edinburgh Business School, 29 Buccleuch Place, Edinburgh, EH8 9JS, UK |
| BookMark | eNp9kE1PwzAMQCM0JLbBH-BUiXNLnCVri7hM41OaxAXOUZa4U6YuKUk6iX9Pq3HisJNjy8-O34xMnHdIyC3QAigs7_dFF7ErGGVQUF7QBVyQKZSc5wtRVxMypbUoc1ExekVmMe4ppcBKmBL9hNh1tsOHbJVFPNg89h2Go41oshZVcNbtssaHzA9llcZMe2dsst5lAbXfudPbN9mhb5PNu-BNr1M2Tm2tw3hNLhvVRrz5i3Py9fL8uX7LNx-v7-vVJtcLVqVc8XLLla6wrPVW0drAtmqEEkojNqJcMm401mPRgOFKwBZLxQ2lNRiAyizm5O40d_jBd48xyb3vgxtWSiYEMEE5g6GrOnXp4GMM2EhtkxpPSEHZVgKVo1K5l6NSOSqVlMtB6YCyf2gX7EGFn_PQ4wnC4fSjxSCjtug0GjvoS9J4ew7_BZWXlP8 |
| CitedBy_id | crossref_primary_10_1038_s41598_025_87283_2 crossref_primary_10_1016_j_measurement_2024_115415 crossref_primary_10_1088_2631_8695_ad4cb2 crossref_primary_10_1016_j_jpse_2025_100314 crossref_primary_10_1016_j_jtice_2023_105098 crossref_primary_10_1016_j_psep_2022_07_016 crossref_primary_10_1016_j_aei_2022_101687 crossref_primary_10_1016_j_psep_2021_09_046 crossref_primary_10_1080_15567036_2022_2087806 crossref_primary_10_1016_j_ress_2022_108932 crossref_primary_10_1016_j_energy_2022_125976 crossref_primary_10_1016_j_jprocont_2024_103283 crossref_primary_10_1016_j_petsci_2023_04_016 crossref_primary_10_1061_JPSEA2_PSENG_1464 crossref_primary_10_3389_fenrg_2021_759498 crossref_primary_10_1016_j_energy_2022_124689 crossref_primary_10_1016_j_esr_2022_100933 crossref_primary_10_1016_j_engappai_2024_109785 crossref_primary_10_1016_j_psep_2022_12_019 crossref_primary_10_1061__ASCE_PS_1949_1204_0000641 crossref_primary_10_1016_j_psep_2022_08_036 crossref_primary_10_1016_j_measurement_2025_116857 crossref_primary_10_1016_j_psep_2022_04_036 crossref_primary_10_1016_j_energy_2023_127452 crossref_primary_10_1016_j_rser_2022_113046 crossref_primary_10_1016_j_psep_2021_09_033 crossref_primary_10_1016_j_energy_2022_125325 crossref_primary_10_1016_j_engappai_2025_111564 crossref_primary_10_1016_j_enbenv_2024_03_001 crossref_primary_10_1061_JPSEA2_PSENG_1451 crossref_primary_10_3390_pr9081456 crossref_primary_10_1016_j_energy_2023_128810 crossref_primary_10_1016_j_energy_2022_125025 crossref_primary_10_1016_j_jii_2024_100726 crossref_primary_10_1016_j_psep_2022_03_049 crossref_primary_10_1109_ACCESS_2024_3406604 crossref_primary_10_1016_j_cherd_2022_12_036 crossref_primary_10_1061_JPSEA2_PSENG_1490 crossref_primary_10_1109_TIM_2025_3566817 crossref_primary_10_1016_j_compchemeng_2022_107733 |
| Cites_doi | 10.1016/j.compbiolchem.2020.107269 10.1016/j.petrol.2019.02.045 10.1016/j.jhydrol.2020.124700 10.1016/j.petrol.2005.05.004 10.1016/j.eswa.2011.08.170 10.1016/j.ins.2020.05.038 10.1016/j.ins.2020.06.062 10.1016/j.psep.2013.02.001 10.1016/j.measurement.2020.108284 10.1016/j.psep.2020.06.047 10.1016/j.psep.2019.11.038 10.1109/ICIEA.2009.5138292 10.1016/j.measurement.2019.01.029 10.1016/j.petrol.2018.11.067 10.1016/j.psep.2018.06.017 10.1016/j.apenergy.2020.115123 10.1016/j.energy.2015.03.084 10.1016/j.jlp.2011.06.017 10.1016/j.compchemeng.2021.107290 10.1016/j.compchemeng.2020.106755 10.1016/j.measurement.2013.12.009 10.1016/j.ijcip.2020.100389 10.1016/j.compbiomed.2020.103831 10.1016/j.ipm.2009.03.002 |
| ContentType | Journal Article |
| Copyright | 2021 Institution of Chemical Engineers Copyright Elsevier Science Ltd. Jun 2021 |
| Copyright_xml | – notice: 2021 Institution of Chemical Engineers – notice: Copyright Elsevier Science Ltd. Jun 2021 |
| DBID | AAYXX CITATION 7ST 7TB 7U7 8FD C1K FR3 KR7 SOI |
| DOI | 10.1016/j.psep.2021.04.031 |
| DatabaseName | CrossRef Environment Abstracts Mechanical & Transportation Engineering Abstracts Toxicology Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Civil Engineering Abstracts Environment Abstracts |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Toxicology Abstracts Mechanical & Transportation Engineering Abstracts Engineering Research Database Environment Abstracts Environmental Sciences and Pollution Management |
| DatabaseTitleList | Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Environmental Sciences |
| EISSN | 1744-3598 |
| EndPage | 521 |
| ExternalDocumentID | 10_1016_j_psep_2021_04_031 S0957582021002172 |
| GroupedDBID | --K --M -QF .~1 0R~ 123 1B1 1~. 1~5 3EH 4.4 457 4G. 4P2 53G 5VS 7-5 71M 8P~ 8WZ A6W AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABFNM ABFRF ABFYP ABJNI ABLST ABMAC ABNUV ABXDB ABYKQ ACDAQ ACGFO ACRLP ADBBV ADEWK ADEZE ADMUD AEBSH AEFWE AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHIDL AHPOS AIAGR AIEXJ AIKHN AITUG AJBFU AJOXV AKIFW AKURH ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BKOJK BLECG BLXMC CAG COF CS3 DU5 EBS EDH EFJIC EFLBG EJD ENUVR EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN GBLVA HVGLF HZ~ I-F IHE J1W JARJE KCYFY KOM M41 ML. MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SDF SDG SES SJN SPC SPCBC SSG SSJ SSR SSZ T5K UNMZH XFK ZE2 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADMLS ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP BANNL CITATION EFKBS ~HD 7ST 7TB 7U7 8FD AGCQF C1K FR3 KR7 SOI |
| ID | FETCH-LOGICAL-c328t-a47b4ac8e79cba09d1b8f5a5aceef57624dce91b8fd1d4a51be7a4d0091d118d3 |
| ISICitedReferencesCount | 43 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000654679800012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-5820 |
| IngestDate | Wed Aug 13 05:55:08 EDT 2025 Tue Nov 18 21:21:59 EST 2025 Sat Nov 29 07:05:36 EST 2025 Fri Feb 23 02:43:42 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | LSTM Sensitivity analysis AE MRI GMM KS KNN SVM BP Pipeline DT XGB RF Operating condition recognition Semi-supervised learning SAE GB MLNN SCADA |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-a47b4ac8e79cba09d1b8f5a5aceef57624dce91b8fd1d4a51be7a4d0091d118d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-3209-9992 0000-0002-9867-6898 |
| PQID | 2551250421 |
| PQPubID | 2047550 |
| PageCount | 12 |
| ParticipantIDs | proquest_journals_2551250421 crossref_citationtrail_10_1016_j_psep_2021_04_031 crossref_primary_10_1016_j_psep_2021_04_031 elsevier_sciencedirect_doi_10_1016_j_psep_2021_04_031 |
| PublicationCentury | 2000 |
| PublicationDate | June 2021 2021-06-00 20210601 |
| PublicationDateYYYYMMDD | 2021-06-01 |
| PublicationDate_xml | – month: 06 year: 2021 text: June 2021 |
| PublicationDecade | 2020 |
| PublicationPlace | Rugby |
| PublicationPlace_xml | – name: Rugby |
| PublicationTitle | Process safety and environmental protection |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V Elsevier Science Ltd |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier Science Ltd |
| References | Liang, Zhang (bib0070) 2012; 25 Zhang, Ye, Wei, Dongliang, Zhaohui (bib0150) 2009 Rai, Kim (bib0100) 2021; 167 Chen, Liu, Achuthan, Zhang (bib0015) 2020 Sokolova, Lapalme (bib0115) 2009; 45 Zhang, Zhang, He, Li, Xu, Gong (bib0170) 2021 Saha, Santara, Mitra, Chakraborty, Nanjundiah (bib0110) 2020 Zhang, Wu, Hu, Ni (bib0165) 2018; 117 Ao, Li, Zhu, Ali, Yang (bib0005) 2019; 174 Li, Tian, Jiang, Yan (bib0065) 2021; 542 Pontiggia, Vairo, Fabiano (bib0095) 2020 Yang, Ding (bib0130) 2020; 533 da Silva, Morooka, Guilherme, da Fonseca, Mendes (bib0030) 2005; 49 Wang, Zhang, Chang, Li (bib0125) 2020; 584 He, Wang, Wu, Liu (bib0040) 2020 Zhang, Li, Chen, Zhang, Jin (bib0155) 2009; 7 Chetouani (bib0020) 2014; 92 Ye, Zhang, Wei, Dongliang, Zhaohui (bib0140) 2009 Mandal, Chan, Tiwari (bib0080) 2012; 39 Zheng, Dai, Liang, Liao, Zhang (bib0175) 2020 Law, Ghosh (bib0060) 2019 Arian, Hariri, Mehridehnavi, Fassihi, Ghasemi (bib0010) 2020; 86 Zhou, Zhang, Qiu, Liang, Wu, Xiang, Yan (bib0190) 2019 Zhou, Gelder, Liang, Zhang (bib0200) 2020 Halim, Yu, Escobar, Quddus (bib0035) 2020; 143 Pathy, Balasubramanian (bib0090) 2020 Kingma, Ba (bib0055) 2014 Zhang, Chen, Li, Jin (bib0160) 2014; 49 Zhou, Daamen, Vellinga, Hoogendoorn (bib0195) 2019 Jian, Huaguang (bib0045) 2004 Liu, Zang, Liu, Ma, Fu (bib0075) 2019; 138 Sabah, Talebkeikhah, Agin, Telebkeikhah, Hasheminasab (bib0105) 2019 Yu, Zhang (bib0145) 2020 Zheng, Liang, Xu, Wang, Zheng, Li, Liao, Zhang (bib0180) 2021 Ye, Zhang, Liang, Wang (bib0135) 2009 Jiang, Xi, Rahman, Zhang (bib0050) 2020; 271 Szoplik (bib0120) 2015; 85 Cui, Quddus, Mashuga (bib0025) 2020; 134 Messner, Fediuk, Swatek, Scheidl, Pernkopf (bib0085) 2020 Zheng, Zhao (bib0185) 2020; 135 Mandal (10.1016/j.psep.2021.04.031_bib0080) 2012; 39 Szoplik (10.1016/j.psep.2021.04.031_bib0120) 2015; 85 He (10.1016/j.psep.2021.04.031_bib0040) 2020 Rai (10.1016/j.psep.2021.04.031_bib0100) 2021; 167 Yang (10.1016/j.psep.2021.04.031_bib0130) 2020; 533 Li (10.1016/j.psep.2021.04.031_bib0065) 2021; 542 Zhang (10.1016/j.psep.2021.04.031_bib0165) 2018; 117 Ye (10.1016/j.psep.2021.04.031_bib0135) 2009 Pontiggia (10.1016/j.psep.2021.04.031_bib0095) 2020 Sokolova (10.1016/j.psep.2021.04.031_bib0115) 2009; 45 Zhang (10.1016/j.psep.2021.04.031_bib0155) 2009; 7 Ao (10.1016/j.psep.2021.04.031_bib0005) 2019; 174 Zhou (10.1016/j.psep.2021.04.031_bib0200) 2020 Wang (10.1016/j.psep.2021.04.031_bib0125) 2020; 584 Ye (10.1016/j.psep.2021.04.031_bib0140) 2009 Zheng (10.1016/j.psep.2021.04.031_bib0185) 2020; 135 Jian (10.1016/j.psep.2021.04.031_bib0045) 2004 Halim (10.1016/j.psep.2021.04.031_bib0035) 2020; 143 Jiang (10.1016/j.psep.2021.04.031_bib0050) 2020; 271 Liu (10.1016/j.psep.2021.04.031_bib0075) 2019; 138 Yu (10.1016/j.psep.2021.04.031_bib0145) 2020 Zheng (10.1016/j.psep.2021.04.031_bib0180) 2021 Saha (10.1016/j.psep.2021.04.031_bib0110) 2020 Zheng (10.1016/j.psep.2021.04.031_bib0175) 2020 Arian (10.1016/j.psep.2021.04.031_bib0010) 2020; 86 Zhang (10.1016/j.psep.2021.04.031_bib0150) 2009 da Silva (10.1016/j.psep.2021.04.031_bib0030) 2005; 49 Liang (10.1016/j.psep.2021.04.031_bib0070) 2012; 25 Chen (10.1016/j.psep.2021.04.031_bib0015) 2020 Chetouani (10.1016/j.psep.2021.04.031_bib0020) 2014; 92 Kingma (10.1016/j.psep.2021.04.031_bib0055) 2014 Pathy (10.1016/j.psep.2021.04.031_bib0090) 2020 Messner (10.1016/j.psep.2021.04.031_bib0085) 2020 Cui (10.1016/j.psep.2021.04.031_bib0025) 2020; 134 Sabah (10.1016/j.psep.2021.04.031_bib0105) 2019 Zhang (10.1016/j.psep.2021.04.031_bib0160) 2014; 49 Zhang (10.1016/j.psep.2021.04.031_bib0170) 2021 Zhou (10.1016/j.psep.2021.04.031_bib0195) 2019 Law (10.1016/j.psep.2021.04.031_bib0060) 2019 Zhou (10.1016/j.psep.2021.04.031_bib0190) 2019 |
| References_xml | – year: 2020 ident: bib0110 article-title: Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model publication-title: Int. J. Forecast. – start-page: 121 year: 2019 ident: bib0190 article-title: A hybrid time MILP model for the pump scheduling of multi-product pipelines based on the rigorous description of the pipeline hydraulic loss changes publication-title: Comput. Chem. Eng. – start-page: 682 year: 2009 end-page: 686 ident: bib0135 article-title: Fuzzy C-means algorithm in work condition recognition of oil pipeline publication-title: 2009 4th IEEE Conference on Industrial Electronics and Applications – start-page: 204 year: 2020 ident: bib0200 article-title: An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure publication-title: Reliab. Eng. Syst. Saf. – volume: 143 start-page: 348 year: 2020 end-page: 360 ident: bib0035 article-title: Towards a causal model from pipeline incident data analysis publication-title: Process. Saf. Environ. Prot. – volume: 271 year: 2020 ident: bib0050 article-title: Prediction of output power with artificial neural network using extended datasets for Stirling engines publication-title: Appl. Energy – year: 2021 ident: bib0170 article-title: Intelligent feature recognition for STEP-NC-compliant manufacturing based on artificial bee colony algorithm and back propagation neural network publication-title: J. Manuf. Syst. – start-page: 175 year: 2019 ident: bib0195 article-title: Ship classification based on ship behavior clustering from AIS data publication-title: Ocean. Eng. – volume: 86 year: 2020 ident: bib0010 article-title: Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm publication-title: Comput. Biol. Chem. – volume: 92 start-page: 206 year: 2014 end-page: 214 ident: bib0020 article-title: A sequential probability ratio test (SPRT) to detect changes and process safety monitoring publication-title: Process. Saf. Environ. Prot. – year: 2020 ident: bib0175 article-title: An online real-time estimation tool of leakage parameters for hazardous liquid pipelines publication-title: International Journal of Critical Infrastructure Protection – year: 2020 ident: bib0040 article-title: Automatic defects detection and classification of low carbon steel WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network publication-title: Measurement – volume: 167 year: 2021 ident: bib0100 article-title: A novel pipeline leak detection approach independent of prior failure information publication-title: Measurement – start-page: 220 year: 2009 end-page: 224 ident: bib0150 article-title: A novel BP algorithm for pipeline condition recognition publication-title: 2009 WRI World Congress on Computer Science and Information Engineering – volume: 117 start-page: 694 year: 2018 end-page: 703 ident: bib0165 article-title: A probabilistic analysis model of oil pipeline accidents based on an integrated Event-Evolution-Bayesian (EEB) model publication-title: Process. Saf. Environ. Prot. – start-page: 358 year: 2019 ident: bib0060 article-title: Multi-label classification using a cascade of stacked autoencoder and extreme learning machines publication-title: Neurocomputing – year: 2021 ident: bib0180 article-title: Deeppipe: a customized generative model for estimations of liquid pipeline leakage parameters publication-title: Computers & Chemical Engineering – year: 2019 ident: bib0105 article-title: Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: a case study from Marun oil field publication-title: J. Pet. Sci. Eng. – start-page: 3134 year: 2004 end-page: 3137 ident: bib0045 article-title: Oil pipeline leak detection and location using double sensors pressure gradient method publication-title: Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No. 04EX788) – start-page: 218 year: 2020 ident: bib0015 article-title: A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network publication-title: Ocean. Eng. – start-page: 50 year: 2020 ident: bib0090 article-title: Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods publication-title: Algal Res. – volume: 7 start-page: 337 year: 2009 end-page: 341 ident: bib0155 article-title: Recognition method for oil pipeline leak based on chaotic characteristics publication-title: Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering – volume: 45 start-page: 427 year: 2009 end-page: 437 ident: bib0115 article-title: A systematic analysis of performance measures for classification tasks publication-title: Inf. Process. Manag. – volume: 138 start-page: 433 year: 2019 end-page: 445 ident: bib0075 article-title: A leak detection method for oil pipeline based on markov feature and two-stage decision scheme publication-title: Measurement – volume: 85 start-page: 208 year: 2015 end-page: 220 ident: bib0120 article-title: Forecasting of natural gas consumption with artificial neural networks publication-title: Energy – volume: 49 start-page: 223 year: 2005 end-page: 238 ident: bib0030 article-title: Leak detection in petroleum pipelines using a fuzzy system publication-title: J. Pet. Sci. Eng. – volume: 584 year: 2020 ident: bib0125 article-title: Deep learning of subsurface flow via theory-guided neural network publication-title: J. Hydrol. (Amst) – start-page: 646 year: 2009 end-page: 650 ident: bib0140 article-title: Oil pipeline work conditions clustering based on simulated annealing K-means algorithm publication-title: 2009 WRI World Congress on Computer Science and Information Engineering – volume: 25 start-page: 60 year: 2012 end-page: 69 ident: bib0070 article-title: A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model publication-title: J. Loss Prev. Process Ind. – year: 2020 ident: bib0085 article-title: Multi-channel lung sound classification with convolutional recurrent neural networks publication-title: Comput. Biol. Med. – volume: 49 start-page: 382 year: 2014 end-page: 389 ident: bib0160 article-title: Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter publication-title: Measurement – volume: 134 start-page: 178 year: 2020 end-page: 188 ident: bib0025 article-title: Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines publication-title: Process. Saf. Environ. Prot. – volume: 542 start-page: 302 year: 2021 end-page: 316 ident: bib0065 article-title: Distributed-ensemble stacked autoencoder model for non-linear process monitoring publication-title: Inf. Sci. (Ny) – volume: 135 year: 2020 ident: bib0185 article-title: A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis publication-title: Comput. Chem. Eng. – volume: 39 start-page: 3071 year: 2012 end-page: 3080 ident: bib0080 article-title: Leak detection of pipeline: an integrated approach of rough set theory and artificial bee colony trained SVM publication-title: Expert Syst. Appl. – start-page: 92 year: 2020 ident: bib0145 article-title: Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes publication-title: J. Process Control – year: 2014 ident: bib0055 article-title: Adam: A Method for Stochastic Optimization – year: 2020 ident: bib0095 article-title: Critical aspects of natural gas pipelines risk assessments. A case-study application on buried layout publication-title: Process Safety and Environmental Protection – volume: 533 start-page: 108 year: 2020 end-page: 119 ident: bib0130 article-title: Associative memory optimized method on deep neural networks for image classification publication-title: Inf. Sci. (Ny) – volume: 174 start-page: 776 year: 2019 end-page: 789 ident: bib0005 article-title: The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling publication-title: J. Pet. Sci. Eng. – start-page: 50 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0090 article-title: Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods publication-title: Algal Res. – year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0095 article-title: Critical aspects of natural gas pipelines risk assessments. A case-study application on buried layout – start-page: 646 year: 2009 ident: 10.1016/j.psep.2021.04.031_bib0140 article-title: Oil pipeline work conditions clustering based on simulated annealing K-means algorithm publication-title: 2009 WRI World Congress on Computer Science and Information Engineering – volume: 86 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0010 article-title: Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2020.107269 – year: 2019 ident: 10.1016/j.psep.2021.04.031_bib0105 article-title: Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: a case study from Marun oil field publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2019.02.045 – volume: 7 start-page: 337 year: 2009 ident: 10.1016/j.psep.2021.04.031_bib0155 article-title: Recognition method for oil pipeline leak based on chaotic characteristics publication-title: Nami Jishu yu Jingmi Gongcheng/Nanotechnology and Precision Engineering – start-page: 358 year: 2019 ident: 10.1016/j.psep.2021.04.031_bib0060 article-title: Multi-label classification using a cascade of stacked autoencoder and extreme learning machines publication-title: Neurocomputing – volume: 584 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0125 article-title: Deep learning of subsurface flow via theory-guided neural network publication-title: J. Hydrol. (Amst) doi: 10.1016/j.jhydrol.2020.124700 – start-page: 218 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0015 article-title: A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network publication-title: Ocean. Eng. – year: 2021 ident: 10.1016/j.psep.2021.04.031_bib0170 article-title: Intelligent feature recognition for STEP-NC-compliant manufacturing based on artificial bee colony algorithm and back propagation neural network publication-title: J. Manuf. Syst. – volume: 49 start-page: 223 year: 2005 ident: 10.1016/j.psep.2021.04.031_bib0030 article-title: Leak detection in petroleum pipelines using a fuzzy system publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2005.05.004 – volume: 39 start-page: 3071 year: 2012 ident: 10.1016/j.psep.2021.04.031_bib0080 article-title: Leak detection of pipeline: an integrated approach of rough set theory and artificial bee colony trained SVM publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.08.170 – volume: 533 start-page: 108 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0130 article-title: Associative memory optimized method on deep neural networks for image classification publication-title: Inf. Sci. (Ny) doi: 10.1016/j.ins.2020.05.038 – volume: 542 start-page: 302 year: 2021 ident: 10.1016/j.psep.2021.04.031_bib0065 article-title: Distributed-ensemble stacked autoencoder model for non-linear process monitoring publication-title: Inf. Sci. (Ny) doi: 10.1016/j.ins.2020.06.062 – volume: 92 start-page: 206 year: 2014 ident: 10.1016/j.psep.2021.04.031_bib0020 article-title: A sequential probability ratio test (SPRT) to detect changes and process safety monitoring publication-title: Process. Saf. Environ. Prot. doi: 10.1016/j.psep.2013.02.001 – volume: 167 year: 2021 ident: 10.1016/j.psep.2021.04.031_bib0100 article-title: A novel pipeline leak detection approach independent of prior failure information publication-title: Measurement doi: 10.1016/j.measurement.2020.108284 – volume: 143 start-page: 348 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0035 article-title: Towards a causal model from pipeline incident data analysis publication-title: Process. Saf. Environ. Prot. doi: 10.1016/j.psep.2020.06.047 – volume: 134 start-page: 178 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0025 article-title: Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines publication-title: Process. Saf. Environ. Prot. doi: 10.1016/j.psep.2019.11.038 – start-page: 3134 year: 2004 ident: 10.1016/j.psep.2021.04.031_bib0045 article-title: Oil pipeline leak detection and location using double sensors pressure gradient method – start-page: 92 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0145 article-title: Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes publication-title: J. Process Control – year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0040 article-title: Automatic defects detection and classification of low carbon steel WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network publication-title: Measurement – start-page: 682 year: 2009 ident: 10.1016/j.psep.2021.04.031_bib0135 article-title: Fuzzy C-means algorithm in work condition recognition of oil pipeline publication-title: 2009 4th IEEE Conference on Industrial Electronics and Applications doi: 10.1109/ICIEA.2009.5138292 – volume: 138 start-page: 433 year: 2019 ident: 10.1016/j.psep.2021.04.031_bib0075 article-title: A leak detection method for oil pipeline based on markov feature and two-stage decision scheme publication-title: Measurement doi: 10.1016/j.measurement.2019.01.029 – volume: 174 start-page: 776 year: 2019 ident: 10.1016/j.psep.2021.04.031_bib0005 article-title: The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2018.11.067 – volume: 117 start-page: 694 year: 2018 ident: 10.1016/j.psep.2021.04.031_bib0165 article-title: A probabilistic analysis model of oil pipeline accidents based on an integrated Event-Evolution-Bayesian (EEB) model publication-title: Process. Saf. Environ. Prot. doi: 10.1016/j.psep.2018.06.017 – start-page: 175 year: 2019 ident: 10.1016/j.psep.2021.04.031_bib0195 article-title: Ship classification based on ship behavior clustering from AIS data publication-title: Ocean. Eng. – volume: 271 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0050 article-title: Prediction of output power with artificial neural network using extended datasets for Stirling engines publication-title: Appl. Energy doi: 10.1016/j.apenergy.2020.115123 – volume: 85 start-page: 208 year: 2015 ident: 10.1016/j.psep.2021.04.031_bib0120 article-title: Forecasting of natural gas consumption with artificial neural networks publication-title: Energy doi: 10.1016/j.energy.2015.03.084 – volume: 25 start-page: 60 year: 2012 ident: 10.1016/j.psep.2021.04.031_bib0070 article-title: A wave change analysis (WCA) method for pipeline leak detection using Gaussian mixture model publication-title: J. Loss Prev. Process Ind. doi: 10.1016/j.jlp.2011.06.017 – year: 2021 ident: 10.1016/j.psep.2021.04.031_bib0180 article-title: Deeppipe: a customized generative model for estimations of liquid pipeline leakage parameters publication-title: Computers & Chemical Engineering doi: 10.1016/j.compchemeng.2021.107290 – volume: 135 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0185 article-title: A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2020.106755 – year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0110 article-title: Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model publication-title: Int. J. Forecast. – volume: 49 start-page: 382 year: 2014 ident: 10.1016/j.psep.2021.04.031_bib0160 article-title: Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter publication-title: Measurement doi: 10.1016/j.measurement.2013.12.009 – year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0175 article-title: An online real-time estimation tool of leakage parameters for hazardous liquid pipelines publication-title: International Journal of Critical Infrastructure Protection doi: 10.1016/j.ijcip.2020.100389 – year: 2014 ident: 10.1016/j.psep.2021.04.031_bib0055 – year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0085 article-title: Multi-channel lung sound classification with convolutional recurrent neural networks publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2020.103831 – start-page: 220 year: 2009 ident: 10.1016/j.psep.2021.04.031_bib0150 article-title: A novel BP algorithm for pipeline condition recognition publication-title: 2009 WRI World Congress on Computer Science and Information Engineering – start-page: 121 year: 2019 ident: 10.1016/j.psep.2021.04.031_bib0190 article-title: A hybrid time MILP model for the pump scheduling of multi-product pipelines based on the rigorous description of the pipeline hydraulic loss changes publication-title: Comput. Chem. Eng. – start-page: 204 year: 2020 ident: 10.1016/j.psep.2021.04.031_bib0200 article-title: An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure publication-title: Reliab. Eng. Syst. Saf. – volume: 45 start-page: 427 year: 2009 ident: 10.1016/j.psep.2021.04.031_bib0115 article-title: A systematic analysis of performance measures for classification tasks publication-title: Inf. Process. Manag. doi: 10.1016/j.ipm.2009.03.002 |
| SSID | ssj0001271 |
| Score | 2.4666162 |
| Snippet | Intelligent operating monitoring of pipelines helps to detect anomalies in time to ensure pipeline safe, reducing potential risk. However, the operating... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 510 |
| SubjectTerms | Anomalies Learning algorithms Machine learning Mathematical models Model accuracy Monitoring Multilayers Neural networks Operating condition recognition Parameters Pipeline Pipeline safety Pipelines Recognition Risk management Safety management Semi-supervised learning Sensitivity analysis |
| Title | Deeppipe: A semi-supervised learning for operating condition recognition of multi-product pipelines |
| URI | https://dx.doi.org/10.1016/j.psep.2021.04.031 https://www.proquest.com/docview/2551250421 |
| Volume | 150 |
| WOSCitedRecordID | wos000654679800012&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1744-3598 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001271 issn: 0957-5820 databaseCode: AIEXJ dateStart: 19961101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLbKxgEOCAYTg4F84FYZxa4TJ9wqVgQITSAGKlys-EcgU0nD0k77b_hXeY6dkHZiAiQuUWU1rtXvy_N7z-99QehJZoUQUaJIZFJN-MRYkvPCktQoLawykW07vD--EcfH6XyevR2NfnS9MOcLUVXpxUVW_1eoYQzAdq2zfwF3PykMwGcAHa4AO1z_CPgja-u6rEPPeWO_laRZ184kNOBcLrpUiCsvXNZOUtm33bqja0eFvqLIO5JtwSGpvS7s2M3r_NJm6NOGXoNxkxeu_tNl4gftc67Ty2tBDE78P3-1oRIY2Pm97MeP1t1gXyhUhoT2p2X1ZbUeDLc53nflMG3BBuVVPpd2qZ8mJCUFiVPmT2qsN8mCc-J0BjdstlerDVY3DpWxfgOPfcv1pb3BpylOn9aNdUKljLYat2EP2tTcfu8W4tbBqH-F1zW0y0ScgeXfnb6azV_3mz1lbUzfLzz0ZfkSwu1f-p3vs-UFtK7NyW10K8QkeOq5dAeNbLWHbg6UKvfQ_myIKA47QnMX6Y5uz_AUb5ENd2TDQDbckw33ZMMDsuFlgTfIhnuy3UMfXsxOnr8k4cUdRE9YuoKnXSie69SKTKs8ygxVaRHncQ4eWQEBLuNG28wNGmp4HlNlRc4NuPvUQMBrJvtop1pW9j7CCVdFolRkWay5VixLmNapgokhbk7M5ADR7i-VOqjau5erLGRXvngqHQzSwSAjLgGGAzTu76m9psuV3447pGTwSr23KYFYV9532MEqg3loJATw1IkGMvrgH6d9iG78epwO0c7qbG0foev6fFU2Z48DPX8CO0_B-w |
| linkProvider | Elsevier |
| 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=Deeppipe%3A+A+semi-supervised+learning+for+operating+condition+recognition+of+multi-product+pipelines&rft.jtitle=Process+safety+and+environmental+protection&rft.au=Zheng%2C+Jianqin&rft.au=Du%2C+Jian&rft.au=Liang%2C+Yongtu&rft.au=Liao%2C+Qi&rft.date=2021-06-01&rft.pub=Elsevier+B.V&rft.issn=0957-5820&rft.eissn=1744-3598&rft.volume=150&rft.spage=510&rft.epage=521&rft_id=info:doi/10.1016%2Fj.psep.2021.04.031&rft.externalDocID=S0957582021002172 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-5820&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-5820&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-5820&client=summon |