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超声波流量计诊断数据集

超声波流量计诊断数据集

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Computer Classification

Kojo Sarfo GyamfiCoventry University, UKgyamfik '@' uni.coventry.ac.ukCraig MarshallNational Engineering Laborat......

数据结构 ? 74.8K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Kojo Sarfo Gyamfi
    Coventry University, UK
    gyamfik '@' uni.coventry.ac.uk

    Craig Marshall
    National Engineering Laboratory, TUV-NEL, UK
    Craig.Marsall '@' tuv-sud.co.uk


    Data Set Information:

    Meter A contains 87 instances of diagnostic parameters for an 8-path liquid ultrasonic flow meter (USM). It has 37 attributes and 2 classes or health states:
    Class '1' - Healthy
    Class '2' - Installation effects

    Meter B contains 92 instances of diagnostic parameters for a 4-path liquid USM. It has 52 attributes and 3 classes:
    Class '1' - Healthy
    Class '2' - Gas injection
    Class '3' - Waxing

    Meter C contains 181 instances of diagnostic parameters for a 4-path liquid USM. It has 44 attributes and 4 classes:
    Class '1' - Healthy
    Class '2' - Gas injection
    Class '3' - Installation effects
    Class '4' - Waxing

    Meter D contains 180 instances of diagnostic parameters for a 4-path liquid USM. It has 44 attributes and 4 classes:
    Class '1' - Healthy
    Class '2' - Gas injection
    Class '3' - Installation effects
    Class '4' - Waxing


    Attribute Information:

    All attributes are continuous, with the exception of the class attribute.

    Meter A
    (1)        -- Flatness ratio
    (2)        -- Symmetry
    (3)        -- Crossflow
    (4)-(11)   -- Flow velocity in each of the eight paths
    (12)-(19)  -- Speed of sound in each of the eight paths
    (20)       -- Average speed of sound in all eight paths
    (21)-(36)  -- Gain at both ends of each of the eight paths
    (37)       -- Class attribute or health state of meter: 1,2

    Meter B
    (1)       -- Profile factor
    (2)       -- Symmetry
    (3)       -- Crossflow
    (4)       -- Swirl angle
    (5)-(8)   -- Flow velocity in each of the four paths
    (9)       -- Average flow velocity in all four paths
    (10)-(13)  -- Speed of sound in each of the four paths
    (14)       -- Average speed of sound in all four paths
    (15)-(22)  -- Signal strength at both ends of each of the four paths
    (23)-(26)  -- Turbulence in each of the four paths
    (27)      -- Meter performance
    (28)-(35) -- Signal quality at both ends of each of the four paths
    (36)-(43) -- Gain at both ends of each of the four paths
    (44)-51   -- Transit time at both ends of each of the four paths
    (52)      -- Class attribute or health state of meter: 1,2,3

    Meters C and D
    (1)       -- Profile factor
    (2)       -- Symmetry
    (3)       -- Crossflow
    (4)-(7)   -- Flow velocity in each of the four paths
    (8)-(11)  -- Speed of sound in each of the four paths
    (12)-(19) -- Signal strength at both ends of each of the four paths
    (20)-(27) -- Signal quality at both ends of each of the four paths
    (28)-(35) -- Gain at both ends of each of the four paths
    (36)-(43) -- Transit time at both ends of each of the four paths
    (44)      -- Class attribute or health state of meter: 1,2,3,4


    Relevant Papers:

    K. S. Gyamfi, J. Brusey, A. Hunt, E. Gaura , a€?Linear dimensionality reduction for classification via a sequential Bayes error minimisation with an application to flow meter diagnostics,a€? Expert Systems with Applications (IF: 3.928), September 2017



    Citation Request:

    K. S. Gyamfi, J. Brusey, A. Hunt, E. Gaura , a€?Linear dimensionality reduction for classification via a sequential Bayes error minimisation with an application to flow meter diagnostics,a€? Expert Systems with Applications (IF: 3.928), September 2017

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