Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos

ilustraciones, diagramas

Autores:
Velandia Cardenas, Diego Alexander
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/82862
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/82862
https://repositorio.unal.edu.co/
Palabra clave:
600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
Procesamiento de datos
Data processing
Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_b354a3efdd011ab1c586aacff768d7a2
oai_identifier_str oai:repositorio.unal.edu.co:unal/82862
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
dc.title.translated.eng.fl_str_mv Fault detection and isolation in a thermocouple network by data-driven techniques
title Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
spellingShingle Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
Procesamiento de datos
Data processing
Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
title_short Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_full Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_fullStr Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_full_unstemmed Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
title_sort Detección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en Datos
dc.creator.fl_str_mv Velandia Cardenas, Diego Alexander
dc.contributor.advisor.none.fl_str_mv Sofrony Esmeral, Jorge
dc.contributor.author.none.fl_str_mv Velandia Cardenas, Diego Alexander
dc.contributor.subjectmatterexpert.none.fl_str_mv López Pulgarín, Erwin José
dc.contributor.orcid.spa.fl_str_mv 0000-0003-4835-1996
dc.contributor.cvlac.spa.fl_str_mv Velandia Cárdenas, Diego Alexander
dc.contributor.researchgate.spa.fl_str_mv Diego_Velandia
dc.contributor.googlescholar.spa.fl_str_mv 364MrlgAAAAJ
dc.subject.ddc.spa.fl_str_mv 600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
topic 600 - Tecnología (Ciencias aplicadas)
000 - Ciencias de la computación, información y obras generales
Procesamiento de datos
Data processing
Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
dc.subject.lemb.spa.fl_str_mv Procesamiento de datos
dc.subject.lemb.eng.fl_str_mv Data processing
dc.subject.proposal.spa.fl_str_mv Aprendizaje supervisado
Data-driven
Detección
Fallas
FDI
Machine Learning
Sensor
Termopar
XGBoost
dc.subject.proposal.eng.fl_str_mv Supervised learning
Data-driven
Detection
Failures
Machine Learning
Sensor
Thermocouple
description ilustraciones, diagramas
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-12-13T21:41:54Z
dc.date.available.none.fl_str_mv 2022-12-13T21:41:54Z
dc.date.issued.none.fl_str_mv 2022-12-07
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/82862
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/82862
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv CMSA, PR032018OP - Manual del Sistema de Control Estructural del Horno Eléctrico 412-FC-01, 02 ed., 2017.
L. Shen, Z. Jiang, W. Gui, C. Yang, Y. Wang, and B. Sun, “Modelling of inner surface temperature field of blast furnace wall based on inverse heat conduction problems,” IFAC-PapersOnLine, vol. 52, pp. 78–83, 2019.
D. A. Tibaduiza, J. León, L. Bonilla, B. Rueda, O. Zurita, J. C. Forero, J. Vitola, D. Segura, E. Forero, and M. Anaya, “GAP MONITORING IN REFURBISHMENT TASKS IN a FERRONICKEL FURNACE AT CERRO MATOSO SA,” in XI International Conference on Structural Dynamics, EASD, 2020.
D. A. Tibaduiza, J. X. Leon-Medina, R. Gomez, J. Ricardo, B. Rueda, O. Zurita, and J. C. Forero, “Structural health monitoring system for furnace refractory wall thickness measurements at cerro matoso sa,” in European Workshop on Structural Health Monitoring (P. Rizzo and A. Milazzo, eds.), (Cham), pp. 414–423, Springer International Publishing, 2021.
B. Zhou, H. Ye, H. Zhang, and M. Li, “Process monitoring of iron-making process in a blast furnace with PCA-based methods,” Control Engineering Practice, vol. 47, no. February 2016, pp. 1–14, 2016.
A. Van Herwaarden and P. Sarro, “Thermal sensors based on the seebeck effect,” in Sensors and Actuators, vol. 10, pp. 321–346, 1986.
I. E. Comission, “IEC 60584-1:2013 - Thermocouples - Part 1: EMF specifications and tolerances,” International Standard, International Electrotechnical Commission, 08 2013.
R. X. Guo, K. Guo, and J. K. Dong, “Fault diagnosis for sensors in a class of nonlinear systems,” IMA Journal of Mathematical Control and Information, vol. 35, no. 2, pp. 375–391, 2018.
T. Zhang, H. Ye, W.Wang, and H. Zhang, “Fault diagnosis for blast furnace ironmaking process based on two-stage principal component analysis,” ISIJ International, vol. 54, no. 10, pp. 2334–2341, 2014.
P. A. Samara, G. N. Fouskitakis, J. S. Sakallariou, and S. D. Fassois, “A statistical method for the detection of sensor abrupt faults in aircraft control systems,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 789–798, 2008.
Y. Wang, Y. Yan, and Q. Wang, “Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit,” Mathematical Problems in Engineering, vol. 2016, 2016.
X. Dai, F. Qin, Z. Gao, K. Pan, and K. Busawon, “Model-based on-line sensor fault detection in Wireless Sensor Actuator Networks,” Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, no. 1, pp. 556–561, 2015.
P. R. J, “Using Analytical Redundancy,” Computing & Control Engineering, vol. 8, no. May, pp. 127–136, 1991.
R. Shahnazi and Q. Zhao, “Adaptive Fuzzy Descriptor Sliding Mode Observer-based Sensor Fault Estimation for Uncertain Nonlinear Systems,” Asian Journal of Control, vol. 18, no. 4, pp. 1478–1488, 2016.
A. S. Willsky and H. L. Jones, “A Generalized Likelihood Ratio Approach to the Detection and Estimation of Jumps in Linear Systems,” IEEE Transactions on Automatic Control, vol. 21, no. 1, pp. 108–112, 1976.
M. A. Djeziri, A. Aitouche, and B. O. Bouamama, “Sensor fault detection of energetic system using modified parity space approach,” Proceedings of the IEEE Conference on Decision and Control, pp. 2578–2583, 2007.
H. Tolouei and M. A. Shoorehdeli, “Nonlinear parity approach to sensor fault detection in pH neutralization system,” 2017 25th Iranian Conference on Electrical Engineering, ICEE 2017, no. Icee, pp. 889–894, 2017.
Y. Cheng, M. D’Arpino, and G. Rizzoni, “Fault diagnosis in lithium-ion battery of hybrid electric aircraft based on structural analysis,” in 2022 IEEE Transportation Electrification Conference & Expo (ITEC), pp. 997–1004, 2022.
H. Shahnazari, P. Mhaskar, J. House, and T. Salsbury, “Fault diagnosis design for heating, ventilation and air conditioning systems,” in 2018 Annual American Control Conference (ACC), pp. 5787–5792, 2018.
D. Chakraborty and H. Elzarka, “Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold,” Energy and Buildings, vol. 185, pp. 326–344, 2019.
K. Pal and J. A. Campbell, “An Application of Rule-Based and Case-Based Reasoning within a Single Legal Knowledge- Based System,” Data Base for Advances in Information Systems, vol. 28, no. 4, pp. 48–63, 1997.
Z. Dong, J. Zhao, J. Duan, M. Wang, and H. Wang, “Research on Agricultural Machinery Fault Diagnosis System Based on Expert System,” Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, no. Imcec, pp. 2057–2060, 2018.
L. A. Zadeh, “Fuzzy Probabilisties and Their Role in Decision Analysis.,” IFAC Proceedings Volumes, vol. 15, no. 1, pp. 15–21, 1982.
K. Raja and R. Leichombam, “Distributed Fault Node Detection and Classification using Fuzzy Logic and Management Scheme for Wireless Sensor Networks,” 2018 10th International Conference on Advanced Computing, ICoAC 2018, pp. 292–298, 2018.
W. He, P. L. Qiao, Z. J. Zhou, G. Y. Hu, Z. C. Feng, and H. Wei, “A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network,” IEEE Access, vol. 6, pp. 9404–9419, 2018.
Jian-Bo Yang, Jun Liu, Jin Wang, How-Sing Sii, and Hong-Wei Wang, “Belief rule-base inference methodology using the evidential reasoning approach-rimer,” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 36, no. 2, pp. 266–285, 2006.
X. Yuan, M. J. Liebelt, P. Shi, and B. J. Phillips, “Development of Rule-Based Agents for Autonomous Parking Systems by Association Rules Mining,” Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2019-July, pp. 1–6, 2019.
Maciol, Andrzej, Jedrusik, Stanislaw, and Palinski, Andrzej, “How to increase effectiveness of inference in rule-based systems,” Proceedings - 2019 2nd International Conference on Artificial Intelligence for Industries, AI4I 2019, pp. 107–110, 2019.
Z. Wang, Y. Zhou, and G. Li, “Anomaly detection by using streaming k-means and batch k-means,” in 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), pp. 11–17, 2020.
K. Tutuncu, I. Cinar, R. Kursun, and M. Koklu, “Edible and poisonous mushrooms classification by machine learning algorithms,” in 2022 11th Mediterranean Conference on Embedded Computing (MECO), pp. 1–4, 2022.
T. Badriyah, N. Sakinah, I. Syarif, and D. R. Syarif, “Machine learning algorithm for stroke disease classification,” in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp. 1–5, 2020.
Y. S¨onmez, T. Tuncer, H. G¨okal, and E. Avci, “Phishing web sites features classification based on extreme learning machine,” in 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1–5, 2018.
K. Pal and B. V. Patel, “Automatic multiclass document classification of hindi poems using machine learning techniques,” in 2020 International Conference for Emerging Technology (INCET), pp. 1–5, 2020.
M. N. Hasan and I. Koo, “Machine learning-based sensor drift fault classification using discrete cosine transform,” in 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), pp. 1–4, 2021.
M. Kubat, An introduction to machine learning. Springer International Publishing, 2017.
J. Verbraeken, M. Wolting, J. Katzy, J. Kloppenburg, T. Verbelen, and J. S. Rellermeyer, “A survey on distributed machine learning,” ACM Computing Surveys, vol. 53, 6 2020.
G. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM SIGKDD Explorations Newsletter, pp. 20–29, 6 2004.
N. Japkowicz and S. Stephen, “The class imbalance problem: A systematic study,” Intelligent data analysis, vol. 6, no. 5, pp. 429–449, 2002.
K. Pal and B. V. Patel, “Data classification with k-fold cross validation and holdout accuracy estimation methods with 5 different machine learning techniques,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 83–87, 2020.
F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms,” IEEE Access, vol. 10, pp. 39700–39715, 2022.
G. M. Suhas Jain, N. Rakesh, K. Pranavi, and L. Bale, “A novel approach in credit card fraud detection system using machine learning techniques,” in 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS), vol. 1, pp. 1–5, 2021.
C. Srinilta and S. Kanharattanachai, “Application of natural neighbor-based algorithm on oversampling smote algorithms,” in 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 217–220, 2021.
S. Sehra, D. Flores, and G. D. Montañez, “Undecidability of underfitting in learning algorithms,” in 2021 2nd International Conference on Computing and Data Science (CDS), pp. 591–594, 2021.
J. Camacho-Olarte, J. E. S. Torres, D. A. G. Jimenez, J. X. L. Medina, R. C. G. Vargas, D. A. V. Cardenas, C. Gutierrez-Osorio, B. Rueda, W. Vargas, D. A. T. Burgos, C. A. P. Bonilla, J. S. Esmeral, and F. Restrepo-Calle, “A data cleaning approach for a structural health monitoring system in a 75 mw electric arc ferronickel furnace,” Engineering Proceedings, vol. 2, no. 1, 2020.
U. N. de Colombia, “Producto 2.6 informe técnico del análisis de correlación y redundancia física de los waffle coolers y plate coolers del horno línea 1 fc01,” Informe Técnico 2.6, Universidad Nacional de Colombia, 2020.
R. Cao, Y. Chen, M. Shen, J. Chen, J. Zhou, C. Wang, and W. Yang, “A simple method to improve the quality of ndvi time-series data by integrating spatiotemporal information with the savitzky-golay filter,” Remote Sensing of Environment, vol. 217, pp. 244–257, 2018.
J. Chen, P. J¨onsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh, “A simple method for reconstructing a high-quality ndvi time-series data set based on the savitzky-golay filter,” Remote Sensing of Environment, vol. 91, pp. 332–344, 6 2004.
S. Mandal, B. Santhi, S. Sridhar, K. Vinolia, and P. Swaminathan, “Minor fault detection of thermocouple sensor in nuclear power plants using time series analysis,” Annals of Nuclear Energy, vol. 134, pp. 383–389, 2019.
V. N. Vapnik, Statistical learning theory. Wiley, 1998.
L. Breiman, “Bagging predictors,” in Machine Learning, vol. 24, pp. 123–140, Springer Nature, 1996.
L. Breiman, “Random forests,” in Machine Learning, vol. 45, pp. 5–32, Springer Nature, 2001.
P. Mekha and N. Teeyasuksaet, “Image classification of rice leaf diseases using random forest algorithm,” in 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, pp. 165–169, 2021.
S. Li and Y. Yan, “Fault arc detection based on time and frequency domain analysis and radom forest,” in 2021 International Conference on Computer Network, Electronic and Automation (ICCNEA), pp. 248–252, 2021.
Q. Yao, J. Wang, L. Yang, H. Su, and G. Zhang, “A fault diagnosis method of engine rotor based on random forests,” in 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1–4, 2016.
R. Fezai, K. Dhibi, M. Mansouri, M. Trabelsi, M. Hajji, K. Bouzrara, H. Nounou, and M. Nounou, “Effective random forest-based fault detection and diagnosis for wind energy conversion systems,” IEEE Sensors Journal, vol. 21, no. 5, pp. 6914–6921, 2021.
J. X. Leon-Medina, J. Camacho-Olarte, B. Rueda, W. Vargas, L. Bonilla, J. Ruiz, J. Sofrony, J. A. Guerra-Gomez, F. Restrepo-Calle, and D. A. Tibaduiza, “Monitoring of the refractory lining in a shielded electric arc furnace: An online multitarget regression trees approach,” Structural Control and Health Monitoring, vol. 29, Nov. 2021.
A. N. Warrier and S. Amuru, “How to choose a neural network architecture? - a modulation classification example,” in 2020 IEEE 3rd 5G World Forum (5GWF), pp. 413–417, 2020.
R. Kanagaraj, E. Elakiya, N. Rajkumar, K. Srinivasan, and S. Sriram, “Fuzzy neural network classification model for multi labeled electricity consumption data set,” in 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1037–1041, 2022.
D. Prabhu, M. Abedin, W. Winfree, and E. Madaras, “Disbond detection through ultrasonic signal classification using an artificial neural network,” in IJCNN-91-Seattle International Joint Conference on Neural Networks, vol. ii, pp. 906 vol.2–, 1991.
F. A. U¸ckun, H. ¨ Ozer, E. Nurbas, and E. Onat, “Direction finding using convolutional neural networks and convolutional recurrent neural networks,” in 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1–4, 2020.
R. Xin, J. Zhang, and Y. Shao, “Complex network classification with convolutional neural network,” Tsinghua Science and Technology, vol. 25, no. 4, pp. 447–457, 2020.
J. X. León-Medina, J. Camacho, C. Gutierrez-Osorio, J. E. Salomón, B. Rueda, W. Vargas, J. Sofrony, F. Restrepo-Calle, C. Pedraza, and D. Tibaduiza, “Temperature prediction using multivariate time series deep learning in the lining of an electric arc furnace for ferronickel production,” Sensors, vol. 21, no. 20, 2021.
D. F. Godoy-Rojas, J. X. Leon-Medina, B. Rueda, W. Vargas, J. Romero, C. Pedraza, F. Pozo, and D. A. Tibaduiza, “Attention-based deep recurrent neural network to forecast the temperature behavior of an electric arc furnace side-wall,” Sensors, vol. 22, no. 4, 2022.
C. Cortes, V. Vapnik, and L. Saitta, “Support-vector networks,” Machine Leaming, vol. 20, pp. 273–297, 1995
L. Devroye, L. Györfi, and G. Lugosi, Vapnik-Chervonenkis Theory, pp. 187–213. New York, NY: Springer New York, 1996.
G. Xu, Z. Cao, B.-G. Hu, and J. C. Principe, “Robust support vector machines based on the rescaled hinge loss function,” Pattern Recognition, vol. 63, pp. 139–148, 2017.
Z. Chen, L. Hong, Y. Gu, M. Wu, and Z. Yan, “A novel support vector machine multiclassification strategy for power transformer fault diagnosis,” in 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), pp. 3368–3373, 2022.
X. Zhang, S. Wang, and Y. Zhao, “Application of support vector machine and least squares vector machine to freight volume forecast,” in 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, pp. 104–107, 2011.
J. Wang, L. Yu, H. Liu, and Y. Pang, “Analysis of power quality disturbance based on improved hht and bpso-svm,” in 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), pp. 748–753, 2022.
R. Kumar, C. M. Sharma, V. M. Chariar, S. Hooda, and R. Beri, “Emotion analysis of news and social media text for stock price prediction using svm-lstm-gru composite model,” in 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), pp. 329–333, 2022.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, (New York, NY, USA), pp. 785–794, Association for Computing Machinery, 2016.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.
L. Jidong and Z. Ran, “Dynamic weighting multi factor stock selection strategy based on xgboost machine learning algorithm,” in 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), pp. 868–872, 2018.
J. Bao, “Multi-features based arrhythmia diagnosis algorithm using xgboost,” in 2020 International Conference on Computing and Data Science (CDS), pp. 454–457, 2020.
Z. Zheng, S. Pan, H. Luo, and Z. Guo, “Porosity prediction based on gs+ga-xgboost,” in 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 1014–1020, 2020.
D. Zhang, L. Qian, B. Mao, C. Huang, B. Huang, and Y. Si, “A data-driven design for fault detection of wind turbines using random forests and xgboost,” IEEE Access, vol. 6, pp. 21020–21031, 2018.
B. Liu, X. Wang, K. Sun, J. Zhao, and L. Li, “Fault diagnosis of photovoltaic array based on xgboost method,” in 2021 IEEE Sustainable Power and Energy Conference (iSPEC), pp. 3733–3738, 2021.
C. Mart´ınez Bencardino, Estad´ıstica y muestreo, p. 308. Colombia: ECOE Ediciones Ltda, 13 ed., 2012.
M. Kubat, Performance Evaluation, pp. 211–228. Springer International Publishing, 2 ed., 2017.
L. Prechelt, Early Stopping — But When?, pp. 53–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
A. A. Ozdemir, P. Seiler, and G. J. Balas, “Wind turbine fault detection using counterbased residual thresholding,” IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 44, pp. 8289–8294, 2011.
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dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
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spelling Atribución-NoComercial 4.0 Internacionalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sofrony Esmeral, Jorgee64c2a109fa22579af8b1761776daf31600Velandia Cardenas, Diego Alexander871fe9e2c7e04d6941f49811800dc8f8600López Pulgarín, Erwin José0000-0003-4835-1996Velandia Cárdenas, Diego AlexanderDiego_Velandia364MrlgAAAAJ2022-12-13T21:41:54Z2022-12-13T21:41:54Z2022-12-07https://repositorio.unal.edu.co/handle/unal/82862Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEn el presente documento se explica el proceso de desarrollo de un modelo para detección y aislamiento de fallos (FDI ) en una red de termopares mediante técnicas basadas en datos. El documento inicia describiendo generalidades del funcionamiento de la planta, conceptos básicos sobre termopares, definición de FDI, su relevancia en la planta y el modo en que este se desarrolla actualmente, lo cual abre paso a la identificación del problema y el planteamiento de los objetivos. El desarrollo del proyecto se divide en 4 etapas, iniciando con el reconocimiento del conjunto de datos disponibles, seguido de un estudio de métricas obtenidas a partir del conjunto de datos y su relación con estados de fallo o normalidad en los termopares, establecimiento de una metodología para el entrenamiento de modelos basados en datos y los resultados obtenidos de su aplicación. El documento finaliza con la determinación de parámetros para la construcción un modelo basado en datos que muestra una precisión superior al 76 %, según pruebas de validación aplicadas, entre otras conclusiones obtenidas del desarrollo del presente proyecto. (Texto tomado de la fuente)The present document explains the development process of a fault detection and isolation (FDI) model for a thermocouple network by data-driven techniques. The begins by describing plant’s functioning generals, thermocouples’ basic concepts, FDI definition, its relevance for the plant and how it is currently performed, which allows the problem’s identification and objectives definition. The project’s development divides into 4 stages, starting by a reconnaissance of available data, followed by a study of metrics obtained from the data set and their linkage to thermocouple’s in fail or normality statuses, establishment of a methodology for training data-driven models and its results. The document finalizes by determining the parameters for the constructions of a data-driven model showing an accuracy over 76 %, according to applied validation tests, among other conclusions from the development of the present project.El presente trabajo fue realizado dentro del marco de la colaboración entre la Universidad Nacional de Colombia y Cerro Matoso S.A, financiada por el Ministerio Colombiano de Ciencia mediante la convocatoria 786: “Convocatoria para el registro de proyectos que aspiran a obtener beneficios tributarios por inversi´on en CTel“. La totalidad de los registros empleados en el presente proyecto son de carácter privado y pertenecen a Cerro Matoso S.A. Dichos registros no pueden ser publicados, compartidos o reproducidos total o parcialmente sin el conocimiento y expresa autorización de Cerro Matoso S.A.MaestríaMagíster en Ingeniería - Automatización Industrialix, 130 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá600 - Tecnología (Ciencias aplicadas)000 - Ciencias de la computación, información y obras generalesProcesamiento de datosData processingAprendizaje supervisadoData-drivenDetecciónFallasFDIMachine LearningSensorTermoparXGBoostSupervised learningData-drivenDetectionFailuresMachine LearningSensorThermocoupleDetección y Aislamiento de Fallas en una Red de Termopares Mediante Técnicas Basadas en DatosFault detection and isolation in a thermocouple network by data-driven techniquesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_71e4c1898caa6e32Texthttp://purl.org/redcol/resource_type/TMCMSA, PR032018OP - Manual del Sistema de Control Estructural del Horno Eléctrico 412-FC-01, 02 ed., 2017.L. Shen, Z. Jiang, W. Gui, C. Yang, Y. Wang, and B. Sun, “Modelling of inner surface temperature field of blast furnace wall based on inverse heat conduction problems,” IFAC-PapersOnLine, vol. 52, pp. 78–83, 2019.D. A. Tibaduiza, J. León, L. Bonilla, B. Rueda, O. Zurita, J. C. Forero, J. Vitola, D. Segura, E. Forero, and M. Anaya, “GAP MONITORING IN REFURBISHMENT TASKS IN a FERRONICKEL FURNACE AT CERRO MATOSO SA,” in XI International Conference on Structural Dynamics, EASD, 2020.D. A. Tibaduiza, J. X. Leon-Medina, R. Gomez, J. Ricardo, B. Rueda, O. Zurita, and J. C. Forero, “Structural health monitoring system for furnace refractory wall thickness measurements at cerro matoso sa,” in European Workshop on Structural Health Monitoring (P. Rizzo and A. Milazzo, eds.), (Cham), pp. 414–423, Springer International Publishing, 2021.B. Zhou, H. Ye, H. Zhang, and M. Li, “Process monitoring of iron-making process in a blast furnace with PCA-based methods,” Control Engineering Practice, vol. 47, no. February 2016, pp. 1–14, 2016.A. Van Herwaarden and P. Sarro, “Thermal sensors based on the seebeck effect,” in Sensors and Actuators, vol. 10, pp. 321–346, 1986.I. E. Comission, “IEC 60584-1:2013 - Thermocouples - Part 1: EMF specifications and tolerances,” International Standard, International Electrotechnical Commission, 08 2013.R. X. Guo, K. Guo, and J. K. Dong, “Fault diagnosis for sensors in a class of nonlinear systems,” IMA Journal of Mathematical Control and Information, vol. 35, no. 2, pp. 375–391, 2018.T. Zhang, H. Ye, W.Wang, and H. Zhang, “Fault diagnosis for blast furnace ironmaking process based on two-stage principal component analysis,” ISIJ International, vol. 54, no. 10, pp. 2334–2341, 2014.P. A. Samara, G. N. Fouskitakis, J. S. Sakallariou, and S. D. Fassois, “A statistical method for the detection of sensor abrupt faults in aircraft control systems,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 789–798, 2008.Y. Wang, Y. Yan, and Q. Wang, “Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit,” Mathematical Problems in Engineering, vol. 2016, 2016.X. Dai, F. Qin, Z. Gao, K. Pan, and K. Busawon, “Model-based on-line sensor fault detection in Wireless Sensor Actuator Networks,” Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, no. 1, pp. 556–561, 2015.P. R. J, “Using Analytical Redundancy,” Computing & Control Engineering, vol. 8, no. May, pp. 127–136, 1991.R. Shahnazi and Q. Zhao, “Adaptive Fuzzy Descriptor Sliding Mode Observer-based Sensor Fault Estimation for Uncertain Nonlinear Systems,” Asian Journal of Control, vol. 18, no. 4, pp. 1478–1488, 2016.A. S. Willsky and H. L. Jones, “A Generalized Likelihood Ratio Approach to the Detection and Estimation of Jumps in Linear Systems,” IEEE Transactions on Automatic Control, vol. 21, no. 1, pp. 108–112, 1976.M. A. Djeziri, A. Aitouche, and B. O. Bouamama, “Sensor fault detection of energetic system using modified parity space approach,” Proceedings of the IEEE Conference on Decision and Control, pp. 2578–2583, 2007.H. Tolouei and M. A. Shoorehdeli, “Nonlinear parity approach to sensor fault detection in pH neutralization system,” 2017 25th Iranian Conference on Electrical Engineering, ICEE 2017, no. Icee, pp. 889–894, 2017.Y. Cheng, M. D’Arpino, and G. Rizzoni, “Fault diagnosis in lithium-ion battery of hybrid electric aircraft based on structural analysis,” in 2022 IEEE Transportation Electrification Conference & Expo (ITEC), pp. 997–1004, 2022.H. Shahnazari, P. Mhaskar, J. House, and T. Salsbury, “Fault diagnosis design for heating, ventilation and air conditioning systems,” in 2018 Annual American Control Conference (ACC), pp. 5787–5792, 2018.D. Chakraborty and H. Elzarka, “Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold,” Energy and Buildings, vol. 185, pp. 326–344, 2019.K. Pal and J. A. Campbell, “An Application of Rule-Based and Case-Based Reasoning within a Single Legal Knowledge- Based System,” Data Base for Advances in Information Systems, vol. 28, no. 4, pp. 48–63, 1997.Z. Dong, J. Zhao, J. Duan, M. Wang, and H. Wang, “Research on Agricultural Machinery Fault Diagnosis System Based on Expert System,” Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, no. Imcec, pp. 2057–2060, 2018.L. A. Zadeh, “Fuzzy Probabilisties and Their Role in Decision Analysis.,” IFAC Proceedings Volumes, vol. 15, no. 1, pp. 15–21, 1982.K. Raja and R. Leichombam, “Distributed Fault Node Detection and Classification using Fuzzy Logic and Management Scheme for Wireless Sensor Networks,” 2018 10th International Conference on Advanced Computing, ICoAC 2018, pp. 292–298, 2018.W. He, P. L. Qiao, Z. J. Zhou, G. Y. Hu, Z. C. Feng, and H. Wei, “A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network,” IEEE Access, vol. 6, pp. 9404–9419, 2018.Jian-Bo Yang, Jun Liu, Jin Wang, How-Sing Sii, and Hong-Wei Wang, “Belief rule-base inference methodology using the evidential reasoning approach-rimer,” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 36, no. 2, pp. 266–285, 2006.X. Yuan, M. J. Liebelt, P. Shi, and B. J. Phillips, “Development of Rule-Based Agents for Autonomous Parking Systems by Association Rules Mining,” Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2019-July, pp. 1–6, 2019.Maciol, Andrzej, Jedrusik, Stanislaw, and Palinski, Andrzej, “How to increase effectiveness of inference in rule-based systems,” Proceedings - 2019 2nd International Conference on Artificial Intelligence for Industries, AI4I 2019, pp. 107–110, 2019.Z. Wang, Y. Zhou, and G. Li, “Anomaly detection by using streaming k-means and batch k-means,” in 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), pp. 11–17, 2020.K. Tutuncu, I. Cinar, R. Kursun, and M. Koklu, “Edible and poisonous mushrooms classification by machine learning algorithms,” in 2022 11th Mediterranean Conference on Embedded Computing (MECO), pp. 1–4, 2022.T. Badriyah, N. Sakinah, I. Syarif, and D. R. Syarif, “Machine learning algorithm for stroke disease classification,” in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp. 1–5, 2020.Y. S¨onmez, T. Tuncer, H. G¨okal, and E. Avci, “Phishing web sites features classification based on extreme learning machine,” in 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1–5, 2018.K. Pal and B. V. Patel, “Automatic multiclass document classification of hindi poems using machine learning techniques,” in 2020 International Conference for Emerging Technology (INCET), pp. 1–5, 2020.M. N. Hasan and I. Koo, “Machine learning-based sensor drift fault classification using discrete cosine transform,” in 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), pp. 1–4, 2021.M. Kubat, An introduction to machine learning. Springer International Publishing, 2017.J. Verbraeken, M. Wolting, J. Katzy, J. Kloppenburg, T. Verbelen, and J. S. Rellermeyer, “A survey on distributed machine learning,” ACM Computing Surveys, vol. 53, 6 2020.G. Batista, R. C. Prati, and M. C. Monard, “A study of the behavior of several methods for balancing machine learning training data,” ACM SIGKDD Explorations Newsletter, pp. 20–29, 6 2004.N. Japkowicz and S. Stephen, “The class imbalance problem: A systematic study,” Intelligent data analysis, vol. 6, no. 5, pp. 429–449, 2002.K. Pal and B. V. Patel, “Data classification with k-fold cross validation and holdout accuracy estimation methods with 5 different machine learning techniques,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 83–87, 2020.F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms,” IEEE Access, vol. 10, pp. 39700–39715, 2022.G. M. Suhas Jain, N. Rakesh, K. Pranavi, and L. Bale, “A novel approach in credit card fraud detection system using machine learning techniques,” in 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS), vol. 1, pp. 1–5, 2021.C. Srinilta and S. Kanharattanachai, “Application of natural neighbor-based algorithm on oversampling smote algorithms,” in 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 217–220, 2021.S. Sehra, D. Flores, and G. D. Montañez, “Undecidability of underfitting in learning algorithms,” in 2021 2nd International Conference on Computing and Data Science (CDS), pp. 591–594, 2021.J. Camacho-Olarte, J. E. S. Torres, D. A. G. Jimenez, J. X. L. Medina, R. C. G. Vargas, D. A. V. Cardenas, C. Gutierrez-Osorio, B. Rueda, W. Vargas, D. A. T. Burgos, C. A. P. Bonilla, J. S. Esmeral, and F. Restrepo-Calle, “A data cleaning approach for a structural health monitoring system in a 75 mw electric arc ferronickel furnace,” Engineering Proceedings, vol. 2, no. 1, 2020.U. N. de Colombia, “Producto 2.6 informe técnico del análisis de correlación y redundancia física de los waffle coolers y plate coolers del horno línea 1 fc01,” Informe Técnico 2.6, Universidad Nacional de Colombia, 2020.R. Cao, Y. Chen, M. Shen, J. Chen, J. Zhou, C. Wang, and W. Yang, “A simple method to improve the quality of ndvi time-series data by integrating spatiotemporal information with the savitzky-golay filter,” Remote Sensing of Environment, vol. 217, pp. 244–257, 2018.J. Chen, P. J¨onsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh, “A simple method for reconstructing a high-quality ndvi time-series data set based on the savitzky-golay filter,” Remote Sensing of Environment, vol. 91, pp. 332–344, 6 2004.S. Mandal, B. Santhi, S. Sridhar, K. Vinolia, and P. Swaminathan, “Minor fault detection of thermocouple sensor in nuclear power plants using time series analysis,” Annals of Nuclear Energy, vol. 134, pp. 383–389, 2019.V. N. Vapnik, Statistical learning theory. Wiley, 1998.L. Breiman, “Bagging predictors,” in Machine Learning, vol. 24, pp. 123–140, Springer Nature, 1996.L. Breiman, “Random forests,” in Machine Learning, vol. 45, pp. 5–32, Springer Nature, 2001.P. Mekha and N. Teeyasuksaet, “Image classification of rice leaf diseases using random forest algorithm,” in 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, pp. 165–169, 2021.S. Li and Y. Yan, “Fault arc detection based on time and frequency domain analysis and radom forest,” in 2021 International Conference on Computer Network, Electronic and Automation (ICCNEA), pp. 248–252, 2021.Q. Yao, J. Wang, L. Yang, H. Su, and G. Zhang, “A fault diagnosis method of engine rotor based on random forests,” in 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1–4, 2016.R. Fezai, K. Dhibi, M. Mansouri, M. Trabelsi, M. Hajji, K. Bouzrara, H. Nounou, and M. Nounou, “Effective random forest-based fault detection and diagnosis for wind energy conversion systems,” IEEE Sensors Journal, vol. 21, no. 5, pp. 6914–6921, 2021.J. X. Leon-Medina, J. Camacho-Olarte, B. Rueda, W. Vargas, L. Bonilla, J. Ruiz, J. Sofrony, J. A. Guerra-Gomez, F. Restrepo-Calle, and D. A. Tibaduiza, “Monitoring of the refractory lining in a shielded electric arc furnace: An online multitarget regression trees approach,” Structural Control and Health Monitoring, vol. 29, Nov. 2021.A. N. Warrier and S. Amuru, “How to choose a neural network architecture? - a modulation classification example,” in 2020 IEEE 3rd 5G World Forum (5GWF), pp. 413–417, 2020.R. Kanagaraj, E. Elakiya, N. Rajkumar, K. Srinivasan, and S. Sriram, “Fuzzy neural network classification model for multi labeled electricity consumption data set,” in 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1037–1041, 2022.D. Prabhu, M. Abedin, W. Winfree, and E. Madaras, “Disbond detection through ultrasonic signal classification using an artificial neural network,” in IJCNN-91-Seattle International Joint Conference on Neural Networks, vol. ii, pp. 906 vol.2–, 1991.F. A. U¸ckun, H. ¨ Ozer, E. Nurbas, and E. Onat, “Direction finding using convolutional neural networks and convolutional recurrent neural networks,” in 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1–4, 2020.R. Xin, J. Zhang, and Y. Shao, “Complex network classification with convolutional neural network,” Tsinghua Science and Technology, vol. 25, no. 4, pp. 447–457, 2020.J. X. León-Medina, J. Camacho, C. Gutierrez-Osorio, J. E. Salomón, B. Rueda, W. Vargas, J. Sofrony, F. Restrepo-Calle, C. Pedraza, and D. Tibaduiza, “Temperature prediction using multivariate time series deep learning in the lining of an electric arc furnace for ferronickel production,” Sensors, vol. 21, no. 20, 2021.D. F. Godoy-Rojas, J. X. Leon-Medina, B. Rueda, W. Vargas, J. Romero, C. Pedraza, F. Pozo, and D. A. Tibaduiza, “Attention-based deep recurrent neural network to forecast the temperature behavior of an electric arc furnace side-wall,” Sensors, vol. 22, no. 4, 2022.C. Cortes, V. Vapnik, and L. Saitta, “Support-vector networks,” Machine Leaming, vol. 20, pp. 273–297, 1995L. Devroye, L. Györfi, and G. Lugosi, Vapnik-Chervonenkis Theory, pp. 187–213. New York, NY: Springer New York, 1996.G. Xu, Z. Cao, B.-G. Hu, and J. C. Principe, “Robust support vector machines based on the rescaled hinge loss function,” Pattern Recognition, vol. 63, pp. 139–148, 2017.Z. Chen, L. Hong, Y. Gu, M. Wu, and Z. Yan, “A novel support vector machine multiclassification strategy for power transformer fault diagnosis,” in 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), pp. 3368–3373, 2022.X. Zhang, S. Wang, and Y. Zhao, “Application of support vector machine and least squares vector machine to freight volume forecast,” in 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, pp. 104–107, 2011.J. Wang, L. Yu, H. Liu, and Y. Pang, “Analysis of power quality disturbance based on improved hht and bpso-svm,” in 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), pp. 748–753, 2022.R. Kumar, C. M. Sharma, V. M. Chariar, S. Hooda, and R. Beri, “Emotion analysis of news and social media text for stock price prediction using svm-lstm-gru composite model,” in 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), pp. 329–333, 2022.T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, (New York, NY, USA), pp. 785–794, Association for Computing Machinery, 2016.J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.L. Jidong and Z. Ran, “Dynamic weighting multi factor stock selection strategy based on xgboost machine learning algorithm,” in 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), pp. 868–872, 2018.J. Bao, “Multi-features based arrhythmia diagnosis algorithm using xgboost,” in 2020 International Conference on Computing and Data Science (CDS), pp. 454–457, 2020.Z. Zheng, S. Pan, H. Luo, and Z. Guo, “Porosity prediction based on gs+ga-xgboost,” in 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 1014–1020, 2020.D. Zhang, L. Qian, B. Mao, C. Huang, B. Huang, and Y. Si, “A data-driven design for fault detection of wind turbines using random forests and xgboost,” IEEE Access, vol. 6, pp. 21020–21031, 2018.B. Liu, X. Wang, K. Sun, J. Zhao, and L. Li, “Fault diagnosis of photovoltaic array based on xgboost method,” in 2021 IEEE Sustainable Power and Energy Conference (iSPEC), pp. 3733–3738, 2021.C. Mart´ınez Bencardino, Estad´ıstica y muestreo, p. 308. Colombia: ECOE Ediciones Ltda, 13 ed., 2012.M. Kubat, Performance Evaluation, pp. 211–228. Springer International Publishing, 2 ed., 2017.L. Prechelt, Early Stopping — But When?, pp. 53–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.A. A. Ozdemir, P. Seiler, and G. J. Balas, “Wind turbine fault detection using counterbased residual thresholding,” IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 44, pp. 8289–8294, 2011.EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82862/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1019054912_2022.pdf1019054912_2022.pdfTesis de Maestría en Ingeniería - Automatización Industrialapplication/pdf14369433https://repositorio.unal.edu.co/bitstream/unal/82862/4/1019054912_2022.pdf3f87fd655443881af9e141a926b98da9MD54THUMBNAIL1019054912_2022.pdf.jpg1019054912_2022.pdf.jpgGenerated Thumbnailimage/jpeg4481https://repositorio.unal.edu.co/bitstream/unal/82862/5/1019054912_2022.pdf.jpg990929e46ac268c27a7807e6a6a5ccadMD55unal/82862oai:repositorio.unal.edu.co:unal/828622023-08-11 23:04:39.835Repositorio Institucional Universidad Nacional de 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