Data-driven development and maintenance of soft-sensors

Janos Abonyi, Barbara Farsang, Tibor Kulcsar

Research output: Conference contribution

2 Citations (Scopus)

Abstract

Product quality related process variables have significant role in advanced process control (APC). Online analyzers and software sensors can provide accurate and timely information for APC systems. In this paper we give an overview of data based soft-sensor development. We show that soft-sensor models of APC require maintenance and demonstrate that statistical quality control (SQC) techniques can be effectively used to automatize the related fault detection tasks.

Original languageEnglish
Title of host publicationSAMI 2014 - IEEE 12th International Symposium on Applied Machine Intelligence and Informatics, Proceedings
PublisherIEEE Computer Society
Pages239-244
Number of pages6
ISBN (Print)9781479934423
DOIs
Publication statusPublished - jan. 1 2014
Event12th IEEE International Symposium on Applied Machine Intelligence and Informatics, SAMI 2014 - Herl'any, Slovakia
Duration: jan. 23 2014jan. 25 2014

Publication series

NameSAMI 2014 - IEEE 12th International Symposium on Applied Machine Intelligence and Informatics, Proceedings

Other

Other12th IEEE International Symposium on Applied Machine Intelligence and Informatics, SAMI 2014
CountrySlovakia
CityHerl'any
Period1/23/141/25/14

    Fingerprint

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Abonyi, J., Farsang, B., & Kulcsar, T. (2014). Data-driven development and maintenance of soft-sensors. In SAMI 2014 - IEEE 12th International Symposium on Applied Machine Intelligence and Informatics, Proceedings (pp. 239-244). [6822414] (SAMI 2014 - IEEE 12th International Symposium on Applied Machine Intelligence and Informatics, Proceedings). IEEE Computer Society. https://doi.org/10.1109/SAMI.2014.6822414