research-article

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

Authors:

Clauber Gomes Bezerra,

Bruno Sielly Jales Costa,

Luiz Affonso Guedes,

Plamen Parvanov AngelovAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 63, Issue C

Pages 134 - 144

https://doi.org/10.1016/j.eswa.2016.06.035

Published: 30 November 2016 Publication History

Abstract

A new approach to fault detection in industrial processes is presented.This approach uses TEDA algorithm and has autonomous learning.A practical application of TEDA algorithm to fault detection problems is presented.TEDA is applied to two different real world industrial fault detection problems. 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 parameters. 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.

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https://dl.acm.org/doi/10.1016/j.eswa.2022.119377
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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 63, Issue C

November 2016

462 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 30 November 2016

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Cited By

Andrade PSilva IDiniz MFlores TCosta DSoares E(2024)Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression TechniqueACM Transactions on Embedded Computing Systems10.1145/359135623:3(1-28)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3591356
Yan XSarkar MLartey BGebru BHomaifar AKarimoddini ATunstel E(2023)An Online Learning Framework for Sensor Fault Diagnosis Analysis in Autonomous CarsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330562024:12(14467-14479)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3305620
Kim HKo JNa KLee HKim HSon JYoon HYoun B(2023)Opt-TCAEExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119377215:COnline publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119377
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Jales Costa BGoh M(2018)Evolving Background Subtraction for Dynamic Lighting Scenarios2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2018.8491497(1-7)Online publication date: 8-Jul-2018
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