Home > Research > Publications & Outputs > An evolving approach to unsupervised and Real-T...

Electronic data

  • Paper_Expert_Systems_with_Applications_Final

    Rights statement: This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 63, 2016 DOI: 10.1016/j.eswa.2016.06.035

    Accepted author manuscript, 811 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

An evolving approach to unsupervised and Real-Time fault detection in industrial processes

Research output: Contribution to journalJournal article

Published

Standard

An evolving approach to unsupervised and Real-Time fault detection in industrial processes. / Gomes Bezerra, Clauber; Costa, Bruno Sielly Jales; Guedes, Luiz Affonso ; Angelov, Plamen Parvanov.

In: Expert Systems with Applications, Vol. 63, 30.11.2016, p. 134-144.

Research output: Contribution to journalJournal article

Harvard

Gomes Bezerra, C, Costa, BSJ, Guedes, LA & Angelov, PP 2016, 'An evolving approach to unsupervised and Real-Time fault detection in industrial processes', Expert Systems with Applications, vol. 63, pp. 134-144. https://doi.org/10.1016/j.eswa.2016.06.035

APA

Gomes Bezerra, C., Costa, B. S. J., Guedes, L. A., & Angelov, P. P. (2016). An evolving approach to unsupervised and Real-Time fault detection in industrial processes. Expert Systems with Applications, 63, 134-144. https://doi.org/10.1016/j.eswa.2016.06.035

Vancouver

Gomes Bezerra C, Costa BSJ, Guedes LA, Angelov PP. An evolving approach to unsupervised and Real-Time fault detection in industrial processes. Expert Systems with Applications. 2016 Nov 30;63:134-144. https://doi.org/10.1016/j.eswa.2016.06.035

Author

Gomes Bezerra, Clauber ; Costa, Bruno Sielly Jales ; Guedes, Luiz Affonso ; Angelov, Plamen Parvanov. / An evolving approach to unsupervised and Real-Time fault detection in industrial processes. In: Expert Systems with Applications. 2016 ; Vol. 63. pp. 134-144.

Bibtex

@article{239bb2e7b6274607bb0328793533ab55,
title = "An evolving approach to unsupervised and Real-Time fault detection in industrial processes",
abstract = "Fault detection in industrial processes is a field of application that has gaining considerable attention in the past few years, resulting in a large variety of techniques and methodologies designed to solve that problem. However, many of the approaches presented in literature require relevant amounts of prior knowledge about the process, such as mathematical models, data distribution and pre-defined parameters. In this paper, we propose the application of TEDA -Typicality and Eccentricity Data Analytics - , a fully autonomous algorithm, to the problem of fault detection in industrial processes. In order to perform fault detection, TEDA analyzes the density of each read data sample, which is calculated based on the distance between that sample and all the others read so far. TEDA is an online algorithm that learns autonomously and does not require any previous knowledge about the process nor any user-defined param-eters. Moreover, it requires minimum computational effort, enabling its use for real-time applications. The efficiency of the proposed approach is demonstrated with two different real world industrial plant data streams that provide “normal” and “faulty” data. The results shown in this paper are very encouraging when compared with traditional fault detection approaches.",
keywords = "fault detection, industrial processes, typicality, eccentricity, TEDA, autonomous learning",
author = "{Gomes Bezerra}, Clauber and Costa, {Bruno Sielly Jales} and Guedes, {Luiz Affonso} and Angelov, {Plamen Parvanov}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 63, 2016 DOI: 10.1016/j.eswa.2016.06.035",
year = "2016",
month = nov
day = "30",
doi = "10.1016/j.eswa.2016.06.035",
language = "English",
volume = "63",
pages = "134--144",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - An evolving approach to unsupervised and Real-Time fault detection in industrial processes

AU - Gomes Bezerra, Clauber

AU - Costa, Bruno Sielly Jales

AU - Guedes, Luiz Affonso

AU - Angelov, Plamen Parvanov

N1 - This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 63, 2016 DOI: 10.1016/j.eswa.2016.06.035

PY - 2016/11/30

Y1 - 2016/11/30

N2 - Fault detection in industrial processes is a field of application that has gaining considerable attention in the past few years, resulting in a large variety of techniques and methodologies designed to solve that problem. However, many of the approaches presented in literature require relevant amounts of prior knowledge about the process, such as mathematical models, data distribution and pre-defined parameters. In this paper, we propose the application of TEDA -Typicality and Eccentricity Data Analytics - , a fully autonomous algorithm, to the problem of fault detection in industrial processes. In order to perform fault detection, TEDA analyzes the density of each read data sample, which is calculated based on the distance between that sample and all the others read so far. TEDA is an online algorithm that learns autonomously and does not require any previous knowledge about the process nor any user-defined param-eters. Moreover, it requires minimum computational effort, enabling its use for real-time applications. The efficiency of the proposed approach is demonstrated with two different real world industrial plant data streams that provide “normal” and “faulty” data. The results shown in this paper are very encouraging when compared with traditional fault detection approaches.

AB - Fault detection in industrial processes is a field of application that has gaining considerable attention in the past few years, resulting in a large variety of techniques and methodologies designed to solve that problem. However, many of the approaches presented in literature require relevant amounts of prior knowledge about the process, such as mathematical models, data distribution and pre-defined parameters. In this paper, we propose the application of TEDA -Typicality and Eccentricity Data Analytics - , a fully autonomous algorithm, to the problem of fault detection in industrial processes. In order to perform fault detection, TEDA analyzes the density of each read data sample, which is calculated based on the distance between that sample and all the others read so far. TEDA is an online algorithm that learns autonomously and does not require any previous knowledge about the process nor any user-defined param-eters. Moreover, it requires minimum computational effort, enabling its use for real-time applications. The efficiency of the proposed approach is demonstrated with two different real world industrial plant data streams that provide “normal” and “faulty” data. The results shown in this paper are very encouraging when compared with traditional fault detection approaches.

KW - fault detection

KW - industrial processes

KW - typicality

KW - eccentricity

KW - TEDA

KW - autonomous learning

U2 - 10.1016/j.eswa.2016.06.035

DO - 10.1016/j.eswa.2016.06.035

M3 - Journal article

VL - 63

SP - 134

EP - 144

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -