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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Online fault detection based on typicality and eccentricity data analytics
AU - Costa, Bruno Sielly Jales
AU - Bezerra, Clauder Gomez
AU - Guedes, Luiz Affonso
AU - Angelov, Plamen Parvanov
N1 - ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2015/7/12
Y1 - 2015/7/12
N2 - Fault detection is a task of major importance in industry nowadays, since that it can considerably reduce the risk of accidents involving human lives, in addition to production and, consequently, financial losses. Therefore, fault detection systems have been largely studied in the past few years, resulting in many different methods and approaches to solve such problem. This paper presents a detailed study on fault detection on industrial processes based on the recently introduced eccentricity and typicality data analytics (TEDA) approach. TEDA is a recursive and non-parametric method, firstly proposed to the general problem of anomaly detection on data streams. It is based on the measures of data density and proximity from each read data point to the analyzed data set. TEDA is an online autonomous learning algorithm that does not require a priori knowledge about the process, is completely free of user- and problem-defined parameters, requires very low computational effort and, thus, is very suitable for real-time applications. The results further presented were generated by the application of TEDA to a pilot plant for industrial process.
AB - Fault detection is a task of major importance in industry nowadays, since that it can considerably reduce the risk of accidents involving human lives, in addition to production and, consequently, financial losses. Therefore, fault detection systems have been largely studied in the past few years, resulting in many different methods and approaches to solve such problem. This paper presents a detailed study on fault detection on industrial processes based on the recently introduced eccentricity and typicality data analytics (TEDA) approach. TEDA is a recursive and non-parametric method, firstly proposed to the general problem of anomaly detection on data streams. It is based on the measures of data density and proximity from each read data point to the analyzed data set. TEDA is an online autonomous learning algorithm that does not require a priori knowledge about the process, is completely free of user- and problem-defined parameters, requires very low computational effort and, thus, is very suitable for real-time applications. The results further presented were generated by the application of TEDA to a pilot plant for industrial process.
KW - fault detection
KW - evolving
KW - self-learning
U2 - 10.1109/IJCNN.2015.7280712
DO - 10.1109/IJCNN.2015.7280712
M3 - Conference contribution/Paper
SP - 1
EP - 6
BT - Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN)
PB - IEEE
ER -