Data based tools for sensors continuous monitoring in industry applications

This paper presents a 10 years experience of data driven models for sensor validation applied for petroleum and natural gas industry. Auto-associative kernel regression has been used as the main modeling method. The models achieved were embedded in software called Sentinell, which is used for sensor...

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Bibliographic Details
Published in:	2015 IEEE 24th International Symposium on Industrial Electronics (ISIE) pp. 600 - 605
Main Authors:	Galotto, L., Brun, A. D. M., Godoy, R. B., Maciel, F. R. R., Pinto, J. O. P.
Format:	Conference Proceeding Journal Article
Language:	English
Published:	IEEE 01-06-2015
Subjects:	Auto-Associative Kernel Regression Computer programs Crude oil Data Based Models Data models Estimation Instruments Kernel Monitoring Natural gas Natural gas industry Offshore drilling rigs Sensors Sensors Monitoring Software Thermoelectricity
Online Access:	Get full text
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Summary:	This paper presents a 10 years experience of data driven models for sensor validation applied for petroleum and natural gas industry. Auto-associative kernel regression has been used as the main modeling method. The models achieved were embedded in software called Sentinell, which is used for sensors diagnosis. The software is being used in a natural gas compression station, and it has been evaluated in other industries such as: refineries, offshore petroleum platforms, and thermoelectric power plants. In this work the theoretical background is presented, as well as the performance metrics indexes used to evaluate the models. The developed methodology and the results in the real plants are presented and discussed. The experience of these previous works might open future applications in high reliability automated processes.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Conference-1 ObjectType-Feature-3 content type line 23 SourceType-Conference Papers & Proceedings-2
ISSN:	2163-5137 2163-5145
DOI:	10.1109/ISIE.2015.7281536