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Ultrasonic flowmeter diagnostics Data Set
Download: Data Folder, Data Set Description

Abstract: Fault diagnosis of four liquid ultrasonic flowmeters

Data Set Characteristics:  

Multivariate

Number of Instances:

540

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

173

Date Donated

2018-01-13

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

20991


Source:

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 , “Linear dimensionality reduction for classification via a sequential Bayes error minimisation with an application to flow meter diagnostics,” Expert Systems with Applications (IF: 3.928), September 2017



Citation Request:

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


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