Bayesian Network Approach for Dragline Reliability Analysis: a Case Study
Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference,...
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| Autor*in: |
Kumar, Deepak [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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© Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Enthalten in: Mining, metallurgy & exploration - [Cham] : Springer International Publishing, 2019, 40(2023), 1 vom: 14. Jan., Seite 347-365 |
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volume:40 ; year:2023 ; number:1 ; day:14 ; month:01 ; pages:347-365 |
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| DOI / URN: |
10.1007/s42461-023-00729-x |
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| 520 | |a Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. | ||
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| 700 | 1 | |a Gupta, Suprakash |4 aut | |
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10.1007/s42461-023-00729-x doi (DE-627)SPR049263560 (SPR)s42461-023-00729-x-e DE-627 ger DE-627 rakwb eng Kumar, Deepak verfasserin (orcid)0000-0001-6092-609X aut Bayesian Network Approach for Dragline Reliability Analysis: a Case Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. Reliability analysis (dpeaa)DE-He213 Dragline (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 Fault tree Analysis (dpeaa)DE-He213 Jana, Debasis aut Gupta, Suprakash aut Yadav, Pawan Kumar aut Enthalten in Mining, metallurgy & exploration [Cham] : Springer International Publishing, 2019 40(2023), 1 vom: 14. Jan., Seite 347-365 (DE-627)1039800637 (DE-600)2947829-7 2524-3470 nnns volume:40 year:2023 number:1 day:14 month:01 pages:347-365 https://dx.doi.org/10.1007/s42461-023-00729-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 40 2023 1 14 01 347-365 |
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10.1007/s42461-023-00729-x doi (DE-627)SPR049263560 (SPR)s42461-023-00729-x-e DE-627 ger DE-627 rakwb eng Kumar, Deepak verfasserin (orcid)0000-0001-6092-609X aut Bayesian Network Approach for Dragline Reliability Analysis: a Case Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. Reliability analysis (dpeaa)DE-He213 Dragline (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 Fault tree Analysis (dpeaa)DE-He213 Jana, Debasis aut Gupta, Suprakash aut Yadav, Pawan Kumar aut Enthalten in Mining, metallurgy & exploration [Cham] : Springer International Publishing, 2019 40(2023), 1 vom: 14. Jan., Seite 347-365 (DE-627)1039800637 (DE-600)2947829-7 2524-3470 nnns volume:40 year:2023 number:1 day:14 month:01 pages:347-365 https://dx.doi.org/10.1007/s42461-023-00729-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 40 2023 1 14 01 347-365 |
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10.1007/s42461-023-00729-x doi (DE-627)SPR049263560 (SPR)s42461-023-00729-x-e DE-627 ger DE-627 rakwb eng Kumar, Deepak verfasserin (orcid)0000-0001-6092-609X aut Bayesian Network Approach for Dragline Reliability Analysis: a Case Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. Reliability analysis (dpeaa)DE-He213 Dragline (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 Fault tree Analysis (dpeaa)DE-He213 Jana, Debasis aut Gupta, Suprakash aut Yadav, Pawan Kumar aut Enthalten in Mining, metallurgy & exploration [Cham] : Springer International Publishing, 2019 40(2023), 1 vom: 14. Jan., Seite 347-365 (DE-627)1039800637 (DE-600)2947829-7 2524-3470 nnns volume:40 year:2023 number:1 day:14 month:01 pages:347-365 https://dx.doi.org/10.1007/s42461-023-00729-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 40 2023 1 14 01 347-365 |
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10.1007/s42461-023-00729-x doi (DE-627)SPR049263560 (SPR)s42461-023-00729-x-e DE-627 ger DE-627 rakwb eng Kumar, Deepak verfasserin (orcid)0000-0001-6092-609X aut Bayesian Network Approach for Dragline Reliability Analysis: a Case Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. Reliability analysis (dpeaa)DE-He213 Dragline (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 Fault tree Analysis (dpeaa)DE-He213 Jana, Debasis aut Gupta, Suprakash aut Yadav, Pawan Kumar aut Enthalten in Mining, metallurgy & exploration [Cham] : Springer International Publishing, 2019 40(2023), 1 vom: 14. Jan., Seite 347-365 (DE-627)1039800637 (DE-600)2947829-7 2524-3470 nnns volume:40 year:2023 number:1 day:14 month:01 pages:347-365 https://dx.doi.org/10.1007/s42461-023-00729-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 40 2023 1 14 01 347-365 |
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10.1007/s42461-023-00729-x doi (DE-627)SPR049263560 (SPR)s42461-023-00729-x-e DE-627 ger DE-627 rakwb eng Kumar, Deepak verfasserin (orcid)0000-0001-6092-609X aut Bayesian Network Approach for Dragline Reliability Analysis: a Case Study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. Reliability analysis (dpeaa)DE-He213 Dragline (dpeaa)DE-He213 Bayesian network (dpeaa)DE-He213 Fault tree Analysis (dpeaa)DE-He213 Jana, Debasis aut Gupta, Suprakash aut Yadav, Pawan Kumar aut Enthalten in Mining, metallurgy & exploration [Cham] : Springer International Publishing, 2019 40(2023), 1 vom: 14. Jan., Seite 347-365 (DE-627)1039800637 (DE-600)2947829-7 2524-3470 nnns volume:40 year:2023 number:1 day:14 month:01 pages:347-365 https://dx.doi.org/10.1007/s42461-023-00729-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 40 2023 1 14 01 347-365 |
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Bayesian Network Approach for Dragline Reliability Analysis: a Case Study |
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Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Draglines are extensively used in Indian mines. A dragline has more than hundreds of components, and it is complex in design. This study involves the evaluation of the reliability of a draglines system using a Bayesian network (BN) model mapped from a fault tree. Based on the BN inference, the reliability estimation, the diagnosis, and the sensitivity analysis are performed. In this paper, the overall reliability of the dragline is estimated as well as the contribution of the subsystems or components in the overall reliability evaluation is presented. The results showed that the three subsystems of the dragline, namely, the dragging mechanism, electrical auxiliary subsystem, and swing mechanism, have the lowest reliability (82.17%, 87.98%, and 91.30%, respectively) after an hour of operation. The overall reliability at the first hour of machine operation is estimated to be 62.03%. The study may provide a reference for future work related to the dragline machine’s reliability design and maintenance. © Society for Mining, Metallurgy & Exploration Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Bayesian Network Approach for Dragline Reliability Analysis: a Case Study |
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Jana, Debasis Gupta, Suprakash Yadav, Pawan Kumar |
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2024-07-04T00:05:15.426Z |
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| score |
7.40158 |