Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine

Based on increasing global energy demand, electric power generation from Internal Combustion Engines (ICE) has increased over the years. On this idea, the industries have adopted different methods and procedures to prevent failures in these engines, achieve an extension of the life cycle of the mach...

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Autores:
Cardenas Escorcia, Yulineth
Carrillo Caballero, Gaylord Enrique
Alviz Meza, Anibal
ALVIZ, ANTISTIO
Portnoy, Ivan
Fajardo Cuadro, Juan
Ocampo Batlle, Eric Alberto
Da-Costa, Edson
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10926
Acceso en línea:
https://hdl.handle.net/11323/10926
https://repositorio.cuc.edu.co/
Palabra clave:
Principal component analysis
Internal combustion engine
Fault detection
Fault diagnosis
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
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dc.title.eng.fl_str_mv Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
title Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
spellingShingle Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
Principal component analysis
Internal combustion engine
Fault detection
Fault diagnosis
title_short Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
title_full Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
title_fullStr Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
title_full_unstemmed Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
title_sort Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
dc.creator.fl_str_mv Cardenas Escorcia, Yulineth
Carrillo Caballero, Gaylord Enrique
Alviz Meza, Anibal
ALVIZ, ANTISTIO
Portnoy, Ivan
Fajardo Cuadro, Juan
Ocampo Batlle, Eric Alberto
Da-Costa, Edson
dc.contributor.author.none.fl_str_mv Cardenas Escorcia, Yulineth
Carrillo Caballero, Gaylord Enrique
Alviz Meza, Anibal
ALVIZ, ANTISTIO
Portnoy, Ivan
Fajardo Cuadro, Juan
Ocampo Batlle, Eric Alberto
Da-Costa, Edson
dc.subject.proposal.eng.fl_str_mv Principal component analysis
Internal combustion engine
topic Principal component analysis
Internal combustion engine
Fault detection
Fault diagnosis
dc.subject.proposal.fra.fl_str_mv Fault detection
Fault diagnosis
description Based on increasing global energy demand, electric power generation from Internal Combustion Engines (ICE) has increased over the years. On this idea, the industries have adopted different methods and procedures to prevent failures in these engines, achieve an extension of the life cycle of the machines, improve their safety, and provide financial savings. For this reason, this work implements a methodology for detecting and identifying failures in a natural gas engine (JGS 612 GS-N. L), based on the integration of Principal Component Analysis (PCA) and alarm streak analysis. A method used to describe a data set in terms of new uncorrelated variables or components. The components are ordered by the amount of original variance they describe, making the technique useful for reducing the dimensionality of a data set. Technically, PCA searches for the projection according to which the data are best represented in terms of least squares, using the T2 and Q statistics. In the initial stage, a PCA-based algorithm was developed to detect abnormal process trends and identify the variables of greater impact when these anomalies arise. In the next stage, an algorithm was developed and implemented, based on the analysis of alarm streaks, to identify the system’s behavior and thus classify fluctuations into either normal operating condition drifts or system failures. The application of the proposed methodology with real operation data of the engine (JGS 612 GS-N. L) shows that the method outperforms operators in detecting and identifying faults, as it performs these tasks considerably earlier than operators.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2024-04-02T15:50:37Z
dc.date.available.none.fl_str_mv 2024-04-02T15:50:37Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Cardenas, Y., Carrillo, G., Alviz, A., Alviz, A., Portnoy, I., Fajardo, J., Ocampo, E., & Da-Costa, E. (2022). Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine. EUREKA: Physics and Engineering, (6), 84-98. https://doi.org/10.21303/2461-4262.2022.002701
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dc.identifier.doi.none.fl_str_mv 10.21303/2461-4262.2022.002701
dc.identifier.eissn.spa.fl_str_mv 2461-4262
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC – Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Cardenas, Y., Carrillo, G., Alviz, A., Alviz, A., Portnoy, I., Fajardo, J., Ocampo, E., & Da-Costa, E. (2022). Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine. EUREKA: Physics and Engineering, (6), 84-98. https://doi.org/10.21303/2461-4262.2022.002701
2461-4254
10.21303/2461-4262.2022.002701
2461-4262
Corporación Universidad de la Costa
REDICUC – Repositorio CUC
url https://hdl.handle.net/11323/10926
https://repositorio.cuc.edu.co/
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language eng
dc.relation.ispartofjournal.spa.fl_str_mv EUREKA, Physics and Engineering
dc.relation.references.spa.fl_str_mv [1] van Schrick, D. (1997). Remarks on Terminology in the Field of Supervision, Fault Detection and Diagnosis. IFAC Proceedings Volumes, 30 (18), 959–964. doi: https://doi.org/10.1016/s1474-6670(17)42524-9
[2] Quiñones-Grueiro, M., Prieto-Moreno, A., Verde, C., Llanes-Santiago, O. (2019). Data-driven monitoring of multimode continuous processes: A review. Chemometrics and Intelligent Laboratory Systems, 189, 56–71. doi: https://doi.org/10.1016/ j.chemolab.2019.03.012
[3] Coussement, A., Gicquel, O., Parente, A. (2013). MG-local-PCA method for reduced order combustion modeling. Proceedings of the Combustion Institute, 34 (1), 1117–1123. doi: https://doi.org/10.1016/j.proci.2012.05.073
[4] Jung, D., Ng, K. Y., Frisk, E., Krysander, M. (2018). Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation. Control Engineering Practice, 80, 146–156. doi: https://doi.org/10.1016/j.conengprac.2018.08.013
[5] Haanchumpol, T., Sudasna-na-Ayudthya, P., Singhtaun, C. (2020). Modern multivariate control chart using spatial signed rank for non-normal process. Engineering Science and Technology, an International Journal, 23 (4), 859–869. doi: https:// doi.org/10.1016/j.jestch.2019.12.001
[6] Isermann, R. (2005). Model-based fault-detection and diagnosis – status and applications. Annual Reviews in Control, 29 (1), 71–85. doi: https://doi.org/10.1016/j.arcontrol.2004.12.002
[7] Jafarian, K., Mobin, M., Jafari-Marandi, R., Rabiei, E. (2018). Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring. Measurement, 128, 527–536. doi: https://doi.org/10.1016/ j.measurement.2018.04.062
[8] Portnoy, I., Melendez, K., Pinzon, H., Sanjuan, M. (2016). An improved weighted recursive PCA algorithm for adaptive fault detection. Control Engineering Practice, 50, 69–83. doi: https://doi.org/10.1016/j.conengprac.2016.02.010
[9] Niu, G., Xiong, L., Qin, X., Pecht, M. (2019). Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains. Mechanical Systems and Signal Processing, 131, 183–198. doi: https://doi.org/10.1016/j.ymssp.2019.05.053
[10] Albarbar, A., Gu, F., Ball, A. D. (2010). Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis. Measurement, 43 (10), 1376–1386. doi: https://doi.org/10.1016/j.measurement.2010.08.003
[11] Shahnazari, H. (2020). Fault diagnosis of nonlinear systems using recurrent neural networks. Chemical Engineering Research and Design, 153, 233–245. doi: https://doi.org/10.1016/j.cherd.2019.09.026
[12] Ahmadi, H., Gholamzadeh, M., Shahmoradi, L., Nilashi, M., Rashvand, P. (2018). Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 161, 145–172. doi: https:// doi.org/10.1016/j.cmpb.2018.04.013
[13] Cardenas, Y. (2019). Fallas en bujías para motores de generación a gas. (Tesis de maestría). Universidad del Atantico.
[14] Camacho, J., Pérez-Villegas, A., García-Teodoro, P., Maciá-Fernández, G. (2016). PCA-based multivariate statistical network monitoring for anomaly detection. Computers & Security, 59, 118–137. doi: https://doi.org/10.1016/j.cose. 2016.02.008
[15] Meglen, R. R. (1992). Examining large databases: a chemometric approach using principal component analysis. Marine Chemistry, 39 (1-3), 217–237. doi: https://doi.org/10.1016/0304-4203(92)90103-h
[16] Aversano, G., Parra-Alvarez, J. C., Isaac, B. J., Smith, S. T., Coussement, A., Gicquel, O., Parente, A. (2019). PCA and Kriging for the efficient exploration of consistency regions in Uncertainty Quantification. Proceedings of the Combustion Institute, 37 (4), 4461–4469. doi: https://doi.org/10.1016/j.proci.2018.07.040
[17] Li, Z., Yan, X., Yuan, C., Peng, Z., Li, L. (2011). Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method. Mechanical Systems and Signal Processing, 25 (7), 2589–2607. doi: https://doi.org/10.1016/j.ymssp.2011.02.017
[18] D. Rosković, A., Grbić, R., Slišković (2011). Fault tolerant system in a process measurement system based on the pca method. MIPRO, 2011 Proceedings of the 34th International Convention, 1646–1651.
[19] Harrou, F., Nounou, M., Nounou, H. (2013). A statistical fault detection strategy using PCA based EWMA control schemes. 2013 9th Asian Control Conference (ASCC). doi: https://doi.org/10.1109/ascc.2013.6606311
[20] Ding, S., Zhang, P., Ding, E., Naik, A., Deng, P., Gui, W. (2010). On the application of PCA technique to fault diagnosis. Tsinghua Science and Technology, 15 (2), 138–144. doi: https://doi.org/10.1016/s1007-0214(10)70043-2
[21] Yin, S., Steven, X. D., Naik, A., Deng, P., Haghani, A. (2010). On PCA-based fault diagnosis techniques. 2010 Conference on Control and Fault-Tolerant Systems (SysTol). doi: https://doi.org/10.1109/systol.2010.5676031
[22] Tong, C., Lan, T., Shi, X. (2017). Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach. Chemometrics and Intelligent Laboratory Systems, 161, 34–42. doi: https://doi.org/10.1016/j.chemolab.2016.11.015
[23] Hu, Z., Chen, Z., Gui, W., Jiang, B. (2014). Adaptive PCA based fault diagnosis scheme in imperial smelting process. ISA Transactions, 53 (5), 1446–1455. doi: https://doi.org/10.1016/j.isatra.2013.12.018
[24] Huang, Y., Shen, L., Liu, H. (2019). Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China. Journal of Cleaner Production, 209, 415–423. doi: https://doi.org/10.1016/ j.jclepro.2018.10.128
[25] Miller, P., Swanson, R. E., Heckler, C. E. (1998). Contribution plots: A missing link in multivariate quality control. Applied mathematics and computer science, 8 (4), 775–792.
[26] Oliveira, J. C. M., Pontes, K. V., Sartori, I., Embiruçu, M. (2017). Fault Detection and Diagnosis in dynamic systems using Weightless Neural Networks. Expert Systems with Applications, 84, 200–219. doi: https://doi.org/10.1016/j.eswa.2017.05.020
[27] Mårtensson, J., Hjalmarsson, H. (2009). Variance-error quantification for identified poles and zeros. Automatica, 45 (11), 2512–2525. doi: https://doi.org/10.1016/j.automatica.2009.08.001
[28] Wu, X. (2015). Study on mean-standard deviation shortest path problem in stochastic and time-dependent networks: A stochastic dominance based approach. Transportation Research Part B: Methodological, 80, 275–290. doi: https://doi.org/10.1016/ j.trb.2015.07.009
[29] Boutellaa, E., Kerdjidj, O., Ghanem, K. (2019). Covariance matrix based fall detection from multiple wearable sensors. Journal of Biomedical Informatics, 94, 103189. doi: https://doi.org/10.1016/j.jbi.2019.103189
[30] Yang, H., Li, S., Li, K. (2012). Order estimation of multivariable ill-conditioned processes based on PCA method. Journal of Process Control, 22 (7), 1397–1403. doi: https://doi.org/10.1016/j.jprocont.2012.06.013
[31] Zumoffen, D. (2008). Desarrollo de Sistemas de Diagnóstico de Fallas Integrado al Diseño de Control Tolerante a Fallas en Procesos Químicos.
[32] Lane, S., Martin, E. B., Morris, A. J., Gower, P. (2003). Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Transactions of the Institute of Measurement and Control, 25 (1), 17–35. doi: https://doi.org/10.1191/0142331203tm071oa
[33] Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., Yin, K. (2003). A review of process fault detection and diagnosis Part III: Process history based methods. Computers and Chemical Engineering, 27, 327–346.
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spelling Atribución 4.0 Internacional (CC BY 4.0)© 2022 Yulineth Cardenas, Gaylord Carrillo, Anibal Alviz, Antistio Alviz, Ivan Portnoy, Juan Fajardo, Eric Ocampo, Edson Da-Costahttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cardenas Escorcia, YulinethCarrillo Caballero, Gaylord EnriqueAlviz Meza, AnibalALVIZ, ANTISTIOPortnoy, IvanFajardo Cuadro, JuanOcampo Batlle, Eric AlbertoDa-Costa, Edson2024-04-02T15:50:37Z2024-04-02T15:50:37Z2022Cardenas, Y., Carrillo, G., Alviz, A., Alviz, A., Portnoy, I., Fajardo, J., Ocampo, E., & Da-Costa, E. (2022). Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine. EUREKA: Physics and Engineering, (6), 84-98. https://doi.org/10.21303/2461-4262.2022.0027012461-4254https://hdl.handle.net/11323/1092610.21303/2461-4262.2022.0027012461-4262Corporación Universidad de la CostaREDICUC – Repositorio CUChttps://repositorio.cuc.edu.co/Based on increasing global energy demand, electric power generation from Internal Combustion Engines (ICE) has increased over the years. On this idea, the industries have adopted different methods and procedures to prevent failures in these engines, achieve an extension of the life cycle of the machines, improve their safety, and provide financial savings. For this reason, this work implements a methodology for detecting and identifying failures in a natural gas engine (JGS 612 GS-N. L), based on the integration of Principal Component Analysis (PCA) and alarm streak analysis. A method used to describe a data set in terms of new uncorrelated variables or components. The components are ordered by the amount of original variance they describe, making the technique useful for reducing the dimensionality of a data set. Technically, PCA searches for the projection according to which the data are best represented in terms of least squares, using the T2 and Q statistics. In the initial stage, a PCA-based algorithm was developed to detect abnormal process trends and identify the variables of greater impact when these anomalies arise. In the next stage, an algorithm was developed and implemented, based on the analysis of alarm streaks, to identify the system’s behavior and thus classify fluctuations into either normal operating condition drifts or system failures. The application of the proposed methodology with real operation data of the engine (JGS 612 GS-N. L) shows that the method outperforms operators in detecting and identifying faults, as it performs these tasks considerably earlier than operators.15 páginasapplication/pdfengScientific Route OÜstoniahttps://journal.eu-jr.eu/engineering/article/view/2701Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engineArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85EUREKA, Physics and Engineering[1] van Schrick, D. (1997). Remarks on Terminology in the Field of Supervision, Fault Detection and Diagnosis. IFAC Proceedings Volumes, 30 (18), 959–964. doi: https://doi.org/10.1016/s1474-6670(17)42524-9[2] Quiñones-Grueiro, M., Prieto-Moreno, A., Verde, C., Llanes-Santiago, O. (2019). Data-driven monitoring of multimode continuous processes: A review. Chemometrics and Intelligent Laboratory Systems, 189, 56–71. doi: https://doi.org/10.1016/ j.chemolab.2019.03.012[3] Coussement, A., Gicquel, O., Parente, A. (2013). MG-local-PCA method for reduced order combustion modeling. Proceedings of the Combustion Institute, 34 (1), 1117–1123. doi: https://doi.org/10.1016/j.proci.2012.05.073[4] Jung, D., Ng, K. Y., Frisk, E., Krysander, M. (2018). Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation. Control Engineering Practice, 80, 146–156. doi: https://doi.org/10.1016/j.conengprac.2018.08.013[5] Haanchumpol, T., Sudasna-na-Ayudthya, P., Singhtaun, C. (2020). Modern multivariate control chart using spatial signed rank for non-normal process. Engineering Science and Technology, an International Journal, 23 (4), 859–869. doi: https:// doi.org/10.1016/j.jestch.2019.12.001[6] Isermann, R. (2005). Model-based fault-detection and diagnosis – status and applications. Annual Reviews in Control, 29 (1), 71–85. doi: https://doi.org/10.1016/j.arcontrol.2004.12.002[7] Jafarian, K., Mobin, M., Jafari-Marandi, R., Rabiei, E. (2018). Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring. Measurement, 128, 527–536. doi: https://doi.org/10.1016/ j.measurement.2018.04.062[8] Portnoy, I., Melendez, K., Pinzon, H., Sanjuan, M. (2016). An improved weighted recursive PCA algorithm for adaptive fault detection. Control Engineering Practice, 50, 69–83. doi: https://doi.org/10.1016/j.conengprac.2016.02.010[9] Niu, G., Xiong, L., Qin, X., Pecht, M. (2019). Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains. Mechanical Systems and Signal Processing, 131, 183–198. doi: https://doi.org/10.1016/j.ymssp.2019.05.053[10] Albarbar, A., Gu, F., Ball, A. D. (2010). Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis. Measurement, 43 (10), 1376–1386. doi: https://doi.org/10.1016/j.measurement.2010.08.003[11] Shahnazari, H. (2020). Fault diagnosis of nonlinear systems using recurrent neural networks. Chemical Engineering Research and Design, 153, 233–245. doi: https://doi.org/10.1016/j.cherd.2019.09.026[12] Ahmadi, H., Gholamzadeh, M., Shahmoradi, L., Nilashi, M., Rashvand, P. (2018). Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 161, 145–172. doi: https:// doi.org/10.1016/j.cmpb.2018.04.013[13] Cardenas, Y. (2019). Fallas en bujías para motores de generación a gas. (Tesis de maestría). Universidad del Atantico.[14] Camacho, J., Pérez-Villegas, A., García-Teodoro, P., Maciá-Fernández, G. (2016). PCA-based multivariate statistical network monitoring for anomaly detection. Computers & Security, 59, 118–137. doi: https://doi.org/10.1016/j.cose. 2016.02.008[15] Meglen, R. R. (1992). Examining large databases: a chemometric approach using principal component analysis. Marine Chemistry, 39 (1-3), 217–237. doi: https://doi.org/10.1016/0304-4203(92)90103-h[16] Aversano, G., Parra-Alvarez, J. C., Isaac, B. J., Smith, S. T., Coussement, A., Gicquel, O., Parente, A. (2019). PCA and Kriging for the efficient exploration of consistency regions in Uncertainty Quantification. Proceedings of the Combustion Institute, 37 (4), 4461–4469. doi: https://doi.org/10.1016/j.proci.2018.07.040[17] Li, Z., Yan, X., Yuan, C., Peng, Z., Li, L. (2011). Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method. Mechanical Systems and Signal Processing, 25 (7), 2589–2607. doi: https://doi.org/10.1016/j.ymssp.2011.02.017[18] D. Rosković, A., Grbić, R., Slišković (2011). Fault tolerant system in a process measurement system based on the pca method. MIPRO, 2011 Proceedings of the 34th International Convention, 1646–1651.[19] Harrou, F., Nounou, M., Nounou, H. (2013). A statistical fault detection strategy using PCA based EWMA control schemes. 2013 9th Asian Control Conference (ASCC). doi: https://doi.org/10.1109/ascc.2013.6606311[20] Ding, S., Zhang, P., Ding, E., Naik, A., Deng, P., Gui, W. (2010). On the application of PCA technique to fault diagnosis. Tsinghua Science and Technology, 15 (2), 138–144. doi: https://doi.org/10.1016/s1007-0214(10)70043-2[21] Yin, S., Steven, X. D., Naik, A., Deng, P., Haghani, A. (2010). On PCA-based fault diagnosis techniques. 2010 Conference on Control and Fault-Tolerant Systems (SysTol). doi: https://doi.org/10.1109/systol.2010.5676031[22] Tong, C., Lan, T., Shi, X. (2017). Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach. 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Computers and Chemical Engineering, 27, 327–346.98846Principal component analysisInternal combustion engineFault detectionFault diagnosisPublicationORIGINALApplication of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine.pdfApplication of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine.pdfArtículoapplication/pdf5035728https://repositorio.cuc.edu.co/bitstreams/f91a43a9-b252-434e-8ca6-8d2ae07d2a22/downloadc0afd373b5eb9eb19ddb70b745ca0a6cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/e0f1a160-12ec-4a46-8cec-8df284baec6e/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTApplication of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine.pdf.txtApplication of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine.pdf.txtExtracted texttext/plain39573https://repositorio.cuc.edu.co/bitstreams/d1049e00-f12d-4954-a92d-0796e3639abc/download228036e8ea2e2eb9b3a71fbcb4ebe421MD53THUMBNAILApplication of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine.pdf.jpgApplication of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine.pdf.jpgGenerated Thumbnailimage/jpeg11130https://repositorio.cuc.edu.co/bitstreams/4ed5e0b6-a538-4e93-a27e-53a0ecf8af85/downloadc258c68509c57bfc3215a2e48c709e58MD5411323/10926oai:repositorio.cuc.edu.co:11323/109262024-09-17 14:24:39.024https://creativecommons.org/licenses/by/4.0/© 2022 Yulineth Cardenas, Gaylord Carrillo, Anibal Alviz, Antistio Alviz, Ivan Portnoy, Juan Fajardo, Eric Ocampo, Edson Da-Costaopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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