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
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -