Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/3956
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dc.contributor.authorMaseko, Moses L.en_US
dc.contributor.authorAgee, John T.en_US
dc.contributor.authorDavidson, Innocenten_US
dc.date.accessioned2022-04-28T14:25:27Z-
dc.date.available2022-04-28T14:25:27Z-
dc.date.issued2022-01-25-
dc.identifier.citationMaseko, M.L., Agee, J.T. and Davidson, I. 2022. Thermocouple signal conditioning using augmented device tables and table look-up neural networks, with validation in J-Thermocouples. Presented at: 2022 30th Southern African Universities Power Engineering Conference (SAUPEC). doi:10.1109/saupec55179.2022.9730718en_US
dc.identifier.isbn9781665468879-
dc.identifier.urihttps://hdl.handle.net/10321/3956-
dc.description.abstractThe relatively high accuracy, large measurement range, and durability of thermocouple devices make these devices to probably be the most-widely used temperature measuring devices in industrial applications. The ability of thermocouples to sense temperature is derived from the generation of thermoelectric voltages arising due to temperature differences between the hot and cold junctions of the thermocouple. Thermocouple temperature measurement processes suffer from inaccuracies arising from both the unwanted or undetected variations in the cold junction temperature of the thermocouple, and nonlinearities in the generated thermoelectric voltage. This paper presents an enhancement of thermocouple temperature measurement using a combination of augmented thermocouple tables generated from thermocouple polynomial functions, look-up MLP neural networks trained to accept the thermocouple output voltage, and the cold or reference junction temperature measurements: to produce improved hot-junction temperature outputs. Experimental validation of the current approach for a J thermocouple, using data from augmented device tables, reproduced the measured temperature values with a worst-case error of 0.0094%.en_US
dc.format.extent4 pen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectArtificial neural networksen_US
dc.subjectThermocouplesen_US
dc.subjectNonlinear, Multilayer perceptronen_US
dc.titleThermocouple signal conditioning using augmented device tables and table look-up neural networks, with validation in J-Thermocouplesen_US
dc.typeConferenceen_US
dc.date.updated2022-04-15T15:23:55Z-
dc.relation.conference2022 30th Southern African Universities Power Engineering Conference (SAUPEC)en_US
dc.identifier.doi10.1109/saupec55179.2022.9730718-
item.languageiso639-1en-
item.openairetypeConference-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Research Publications (Engineering and Built Environment)
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