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.
Gespeichert in:
| 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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code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• 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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data-driven 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