An evolving approach to unsupervised and Real-Time fault detection in industrial processes |
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| Affiliation: | 1. Federal Institute of Rio Grande do Norte - IFRN, Campus EaD, Av. Senador Salgado Filho 1559, Tirol, CEP: 59015-000, Natal, RN, Brazil;2. IFRN - Campus Natal Zona Norte, Rua Brusque 2926, Potengi, CEP 59112-490, Natal, RN, Brazil;3. Federal University of Rio Grande do Norte - UFRN, Department of Computer Engineering and Automation - DCA, Campus Universitrio, Lagoa Nova, CEP: 59078-900, Natal, RN, Brazil;4. Lancaster University, Data Science Group, School of Computing and Communications, Lancaster LA1 4WA, United Kingdom;5. Chair of Excellence, Carlos III University, Madrid, Spain;1. Department of Mathematics, VIT University Vellore Campus, Tamil Nadu, INDIA;2. Department of Mathematics, NIT, Silchar, Assam, India;1. Bioengineering and Telemedicine group, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avd. Complutense 30, 28040, Madrid, Spain;2. CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain;3. Endocrinology and Nutrition department, Hospital de Sabadell. Parc Taulí 1, 08208 Sabadell, Spain;1. Facultad de Ingeniería Informática, Instituto Superior Politécnico “José Antonio Echeverría” (CUJAE), Marianao 19390, La Habana, Cuba;2. Dep. Ingeniería del Software e Inteligencia Artificial, Universidad Complutense de Madrid, Spain;1. Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará, Campus do Pici s/n, Bloco 725, 60455-970, Fortaleza, CE, Brazil;2. Curso de Tecnologia em Manutenção Industrial, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Av. Parque Central s/n, Distrito Industrial I, 61939-140, Maracanaú, CE, Brazil;3. Curso de Engenharia da Computação, Universidade Federal do Ceará, Campus de Sobral, Bloco I - Engenharias, Rua Estanislau Frota s/n, Mucambinho, 62010-560, Sobral, CE, Brazil;4. Curso de Engenharia Elétrica, Universidade Federal do Ceará, Campus de Sobral, Bloco I - Engenharias, Rua Estanislau Frota s/n, Mucambinho, 62010-560, Sobral, CE, Brazil;1. Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC;2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan, ROC;3. Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan, ROC |
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| 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 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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