A model-based health indicator for leak detection in gas pipeline systems
• A novel health index was proposed based on a theoretical model. • The health index showed good robustness under different conditions. • Kullback-Leibler distance was employed to select discriminative features. • Three machine learning techniques were trained to detect pipeline leakage.
Autor*in: |
Xiao, Rui [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level - Kwon, Yeong Min ELSEVIER, 2022, journal of the International Measurement Confederation (IMEKO), Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:171 ; year:2021 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.measurement.2020.108843 |
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ELV052857778 |
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10.1016/j.measurement.2020.108843 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001483.pica (DE-627)ELV052857778 (ELSEVIER)S0263-2241(20)31335-X DE-627 ger DE-627 rakwb eng 530 620 VZ 50.22 bkl 35.07 bkl Xiao, Rui verfasserin aut A model-based health indicator for leak detection in gas pipeline systems 2021 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel health index was proposed based on a theoretical model. • The health index showed good robustness under different conditions. • Kullback-Leibler distance was employed to select discriminative features. • Three machine learning techniques were trained to detect pipeline leakage. Data-driven analysis Elsevier Health indicator Elsevier Gas pipelines Elsevier Acoustic signals Elsevier Leak detection Elsevier Hu, Qunfang oth Li, Jie oth Enthalten in Elsevier Science Kwon, Yeong Min ELSEVIER High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level 2022 journal of the International Measurement Confederation (IMEKO) Amsterdam [u.a.] (DE-627)ELV008789606 volume:171 year:2021 pages:0 https://doi.org/10.1016/j.measurement.2020.108843 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 50.22 Sensorik VZ 35.07 Chemisches Labor chemische Methoden VZ AR 171 2021 0 |
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10.1016/j.measurement.2020.108843 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001483.pica (DE-627)ELV052857778 (ELSEVIER)S0263-2241(20)31335-X DE-627 ger DE-627 rakwb eng 530 620 VZ 50.22 bkl 35.07 bkl Xiao, Rui verfasserin aut A model-based health indicator for leak detection in gas pipeline systems 2021 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel health index was proposed based on a theoretical model. • The health index showed good robustness under different conditions. • Kullback-Leibler distance was employed to select discriminative features. • Three machine learning techniques were trained to detect pipeline leakage. Data-driven analysis Elsevier Health indicator Elsevier Gas pipelines Elsevier Acoustic signals Elsevier Leak detection Elsevier Hu, Qunfang oth Li, Jie oth Enthalten in Elsevier Science Kwon, Yeong Min ELSEVIER High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level 2022 journal of the International Measurement Confederation (IMEKO) Amsterdam [u.a.] (DE-627)ELV008789606 volume:171 year:2021 pages:0 https://doi.org/10.1016/j.measurement.2020.108843 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 50.22 Sensorik VZ 35.07 Chemisches Labor chemische Methoden VZ AR 171 2021 0 |
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