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

Gomes Bezerra, Clauber and Costa, Bruno Sielly Jales and Guedes, Luiz Affonso and Angelov, Plamen Parvanov (2016) An evolving approach to unsupervised and Real-Time fault detection in industrial processes. Expert Systems with Applications, 63. pp. 134-144. ISSN 0957-4174

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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.

Item Type:
Journal Article
Journal or Publication Title:
Expert Systems with Applications
Additional Information:
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
Uncontrolled Keywords:
?? fault detectionindustrial processestypicalityeccentricitytedaautonomous learningartificial intelligencegeneral engineeringcomputer science applicationsengineering(all) ??
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Deposited On:
16 Jun 2016 12:26
Last Modified:
16 Jul 2024 10:07