Abstract
Language | English |
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Number of pages | 6 |
Journal | PHM Society European Conference |
Volume | 4 |
Issue number | 1 |
Publication status | Published - 1 Jul 2018 |
Event | Fourth European Conference of the PHM Society - Utrecht, Netherlands Duration: 3 Jul 2018 → 6 Jul 2018 |
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Keywords
- machine learning
- circuit breakers
- performance
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Automated feature validation of trip coil analysis in condition monitoring of circuit breakers. / Hosseini, Michael; Helm, Joseph; Stephen, Bruce; McArthur, Stephen D. J.
Vol. 4, No. 1, 01.07.2018.Research output: Contribution to journal › Conference Contribution
TY - JOUR
T1 - Automated feature validation of trip coil analysis in condition monitoring of circuit breakers
AU - Hosseini, Michael
AU - Helm, Joseph
AU - Stephen, Bruce
AU - McArthur, Stephen D. J.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - Datasets of historical performance metrics can offer valuable insight into an asset fleet’s health. This is especially so in the context to establishing normal behavior and thresholds of acceptable performance for diagnostic purposes. However, plant performance can often be obscured by data quality issues which introduce artefacts that do not pertain to asset health. This paper utilises a supervised ensemble machine-learning approach to automate the process of filtering maintenance data based on their predicted validity. The results are then presented both in terms of classification performance, and the impact on the distributions directly. This helps to ensure engineers are basing their diagnostic decisions on valid data. The accuracy of the filtration process, and its effect on the final thresholds will be discussed. To illustrate, this paper uses data of varying quality on circuit breaker trip tests obtained from operational medium-voltage circuit-breakers spanning several decades with the aim of providing decision support for switchgear diagnostics.
AB - Datasets of historical performance metrics can offer valuable insight into an asset fleet’s health. This is especially so in the context to establishing normal behavior and thresholds of acceptable performance for diagnostic purposes. However, plant performance can often be obscured by data quality issues which introduce artefacts that do not pertain to asset health. This paper utilises a supervised ensemble machine-learning approach to automate the process of filtering maintenance data based on their predicted validity. The results are then presented both in terms of classification performance, and the impact on the distributions directly. This helps to ensure engineers are basing their diagnostic decisions on valid data. The accuracy of the filtration process, and its effect on the final thresholds will be discussed. To illustrate, this paper uses data of varying quality on circuit breaker trip tests obtained from operational medium-voltage circuit-breakers spanning several decades with the aim of providing decision support for switchgear diagnostics.
KW - machine learning
KW - circuit breakers
KW - performance
UR - https://phmpapers.org/index.php/phme/article/view/453
UR - https://phmpapers.org/index.php/phme/index
M3 - Conference Contribution
VL - 4
IS - 1
ER -