Mostrar el registro sencillo del ítem

Feature relevance estimation for vibration-based condition monitoring of an internal combustion engine

Estimación de características relevantes para el monitoreo de condición de motores de combustión interna a partir de señales de vibración

dc.creatorHernández-Muriel, José Alberto
dc.creatorÁlvarez-Meza, Andrés Marino
dc.creatorEcheverry-Correa, Julián David
dc.creatorOrozco-Gutierrez, Álvaro Ángel
dc.creatorÁlvarez-López, Mauricio Alexánder
dc.date2017-05-02
dc.date.accessioned2021-03-18T21:06:47Z
dc.date.available2021-03-18T21:06:47Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/698
dc.identifier10.22430/22565337.698
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/11697
dc.descriptionCondition monitoring of Internal Combustion Engines (ICE) benefits cost-effective operations in the modern industrial sector. Because of this, vibration signals are commonly monitored as part of a non-invasive approach to ICE analysis. However, vibration-based ICE monitoring poses a challenge due to the properties of this kind of signals. They are highly dynamic and non-stationary, let alone the diverse sources involved in the combustion process. In this paper, we propose a feature relevance estimation strategy for vibration-based ICE analysis. Our approach is divided into three main stages: signal decomposition using an Ensemble Empirical Mode Decomposition algorithm, multi-domain parameter estimation from time and frequency representations, and a supervised feature selection based on the Relief-F technique. Accordingly, we decomposed the vibration signals by using self-adaptive analysis to represent nonlinear and non-stationary time series. Afterwards, time and frequency-based parameters were calculated to code complex and/or non-stationary dynamics. Subsequently, we computed a relevance vector index to measure the contribution of each multi-domain feature to the discrimination of different fuel blend estimation/diagnosis categories for ICE. In particular, we worked with an ICE dataset collected from fuel blends under normal and fault scenarios at different engine speeds to test our approach. Our classification results presented nearly 98% of accuracy after using a k-Nearest Neighbors machine. They reveal the way our approach identifies a relevant subset of features for ICE condition monitoring. One of the benefits is the reduced number of parameters.en-US
dc.descriptionEl monitoreo de condición de motores de combustión interna (MCI) facilita que las operaciones del sector industrial moderno sean más rentables. En este sentido, las señales de vibración comúnmente son empleadas como un enfoque no invasivo para el análisis de MCI. Sin embargo, el monitoreo de MCI basado en vibraciones presenta un desafío relacionado con las propiedades de la señal, la cual es altamente dinámica y noestacionaria, sin mencionar las diversas fuentes presentes durante el proceso de combustión. En este artículo, se propone una estrategia de análisis de relevancia orientada al monitoreo de MCI basado en vibraciones. Este enfoque incorpora tres etapas principales: descomposición de la señal utilizando un algoritmo de Ensemble Empirical Mode Decomposition, estimación de parámetros multi-dominio desde representaciones en tiempo y frecuencia, y una selección supervisada de características basada en Relief-F. Así, las señales de vibración se descomponen utilizando un análisis auto-adaptativo para representar la no-linealidad y no-estacionariedad de las series de tiempo. Luego, para codificar dinámicas complejas y/o no estacionarias, se calculan algunos parámetros en el dominio del tiempo y de la frecuencia. Posteriormente, se calcula un vector de índice de relevancia para cuantificar la contribución de cada una de las características multidominio para discriminar diferentes categorías de estimación de mezcla de combustible y diagnóstico de MCI. Los resultados de clasificación obtenidos (cercanos al 98% de acierto) en una base de datos de MCI, revelan como la propuesta planteada identifica un subconjunto de características relevantes en el monitorio de condición de MCI.es-ES
dc.formatapplication/pdf
dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/698/680
dc.relation/*ref*/J. Flett and G. M. Bone, “Fault detection and diagnosis of diesel engine valve trains,” Mech. Syst. Signal Process., vol. 72–73, pp. 316–327, May 2016. [2] D. Martínez-Rego, O. Fontenla-Romero, A. Alonso-Betanzos, and J. C. Principe, “Fault detection via recurrence time statistics and one-class classification,” Pattern Recognit. Lett., vol. 84, pp. 8–14, Dec. 2016. [3] N. D. Liyanagedera, A. Ratnaweera, and D. I. B. Randeniya, “Vibration signal analysis for fault detection of combustion engine using neural network,” in 2013 IEEE 8th International Conference on Industrial and Information Systems, 2013, pp. 427–432. [4] B. Samimy and G. Rizzoni, “Mechanical signature analysis using time-frequency signal processing: application to internal combustion engine knock detection,” Proc. IEEE, vol. 84, no. 9, pp. 1330–1343, 1996. [5] J.-D. Wu and C.-Q. Chuang, “Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals,” NDT E Int., vol. 38, no. 8, pp. 605–614, Dec. 2005. [6] L. Barelli, G. Bidini, C. Buratti, and R. Mariani, “Diagnosis of internal combustion engine through vibration and acoustic pressure non-intrusive measurements,” Appl. Therm. Eng., vol. 29, no. 8–9, pp. 1707–1713, Jun. 2009. [7] F. Payri, J. M. Luján, J. Martín, and A. Abbad, “Digital signal processing of in-cylinder pressure for combustion diagnosis of internal combustion engines,” Mech. Syst. Signal Process., vol. 24, no. 6, pp. 1767–1784, Aug. 2010. [8] J. Chen, R. B. Randall, and B. Peeters, “Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals,” Mech. Syst. Signal Process., vol. 75, pp. 434–454, Jun. 2016. [9] L. Xu and H. E. Tseng, “Robust model-based fault detection for a roll stability system,” IEEE Trans. Control Syst. Technol., vol. 15, no. 3, pp. 519–528, May 2007. [10] Xuewu Dai, Zhiwei Gao, T. Breikin, and Hong Wang, “Disturbance Attenuation in Fault Detection of Gas Turbine Engines: A Discrete Robust Observer Design,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 39, no. 2, pp. 234–239, Mar. 2009. [11] H. R. Karimi, M. Zapateiro, and N. Luo, “A linear matrix inequality approach to robust fault detection filter design of linear systems with mixed time-varying delays and nonlinear perturbations,” J. Franklin Inst., vol. 347, no. 6, pp. 957–973, Aug. 2010. [12] Y. Zhu and Z. Gao, “Robust observer-based fault detection via evolutionary optimization with applications to wind turbine systems,” in 2014 9th IEEE Conference on Industrial Electronics and Applications, 2014, pp. 1627–1632. [13] S. Ding, Model-based fault diagnosis techniques: design schemes, algorithms, and tools. Springer Science & Business Media, 2008. [14] J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems, vol. 3. Springer Science & Business Media, 2012. [15] Z. Gao, C. Cecati, and S. X. Ding, “A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches,” IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3757–3767, Jun. 2015. [16] G. O. Chandroth, A. J. C. Sharkey, and N. E. Sharkey, “Cylinder pressures and vibration in internal combustion engine condition monitoring,” in Proceedings of Comadem, 1999, vol. 99, pp. 294–297. [17] J. Antoni, J. Daniere, F. Guillet, and R. B. Randall, “Effective vibration analysis of IC engines using cyclostationarity. Part II-new results on the reconstruction of the cylinder pressures,” J. Sound Vib., vol. 257, no. 5, pp. 839–856, Nov. 2002. [18] S. A. Ali and S. Saraswati, “Reconstruction of cylinder pressure using crankshaft speed fluctuations,” in 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, pp. 456–461. [19] X. Zhao, Y. Cheng, and S. Ji, “Combustion parameters identification and correction in diesel engine via vibration acceleration signal,” Appl. Acoust., vol. 116, pp. 205–215, Jan. 2017. [20] T. Denton, Advance Automotive Fault Diagnosis: Auto-motive Technology: Vehicle Maintenance and Repair. Routledge, 2016. [21] A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1483–1510, Oct. 2006. [22] F. Al-Badour, M. Sunar, and L. Cheded, “Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques,” Mech. Syst. Signal Process., vol. 25, no. 6, pp. 2083–2101, Aug. 2011. [23] Y. Shatnawi and M. Al-khassaweneh, “Fault Diagnosis in Internal Combustion Engines Using Extension Neural Network,” IEEE Trans. Ind. Electron., vol. 61, no. 3, pp. 1434–1443, Mar. 2014. [24] Y. Lei, Z. He, and Y. Zi, “Application of the EEMD method to rotor fault diagnosis of rotating machinery,” Mech. Syst. Signal Process., vol. 23, no. 4, pp. 1327–1338, May 2009. [25] M. Buzzoni, E. Mucchi, and G. Dalpiaz, “A CWT-based methodology for piston slap experimental characterization,” Mech. Syst. Signal Process., vol. 86, pp. 16–28, Mar. 2017. [26] Z. Wei, Y. Wang, S. He, and J. Bao, “A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection,” Knowledge-Based Syst., vol. 116, pp. 1–12, Jan. 2017. [27] S. Ericsson, N. Grip, E. Johansson, L.-E. Persson, R. Sjöberg, and J.-O. Strömberg, “Towards automatic detection of local bearing defects in rotating machines,” Mech. Syst. Signal Process., vol. 19, no. 3, pp. 509–535, May 2005. [28] A. Taghizadeh-Alisaraei, B. Ghobadian, T. Tavakoli-Hashjin, S. S. Mohtasebi, A. Rezaei-asl, and M. Azadbakht, “Characterization of engine’s combustion-vibration using diesel and biodiesel fuel blends by time-frequency methods: A case study,” Renew. Energy, vol. 95, pp. 422–432, Sep. 2016. [29] J. Da Wu and J. C. Chen, “Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines,” NDT E Int., vol. 39, no. 4, pp. 304–311, Jun. 2006. [30] A. Moosavian, G. Najafi, B. Ghobadian, M. Mirsalim, S. M. Jafari, and P. Sharghi, “Piston scuffing fault and its identification in an IC engine by vibration analysis,” Appl. Acoust., vol. 102, pp. 40–48, Jan. 2016. [31] Z. Liu, X. Chen, Z. He, and Z. Shen, “LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information,” Sensors, vol. 13, no. 7, pp. 8679–8694, Jul. 2013. [32] S. S. H. Zaidi, S. Aviyente, M. Salman, K.-K. Shin, and E. G. Strangas, “Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1695–1706, May 2011. [33] J. Da Wu and C. H. Liu, “An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network,” Expert Syst. Appl., vol. 36, no. 3, pp. 4278–4286, Apr. 2009. [34] M. A. Rizvi, A. I. Bhatti, and Q. R. Butt, “Hybrid Model of the Gasoline Engine for Misfire Detection,” IEEE Trans. Ind. Electron., vol. 58, no. 8, pp. 3680–3692, Aug. 2011. [35] F. Fiippetti and P. Vas, “Recent developments of induction motor drives fault diagnosis using AI techniques,” in IECON ’98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200), 2000, vol. 4, no. 5, pp. 1966–1973. [36] Q. Hu, Z. He, Z. Zhang, and Y. Zi, “Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble,” Mech. Syst. Signal Process., vol. 21, no. 2, pp. 688–705, Feb. 2007. [37] Y. S. Wang, Q. H. Ma, Q. Zhu, X. T. Liu, and L. H. Zhao, “An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine,” Appl. Acoust., vol. 75, pp. 1–9, Jan. 2014. [38] L. Liang, F. Liu, M. Li, K. He, and G. Xu, “Feature selection for machine fault diagnosis using clustering of non-negation matrix factorization,” Measurement, vol. 94, pp. 295–305, Dec. 2016. [39] M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, “Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3398–3407, Aug. 2013. [40] A. Malhi and R. X. Gao, “PCA-Based Feature Selection Scheme for Machine Defect Classification,” IEEE Trans. Instrum. Meas., vol. 53, no. 6, pp. 1517–1525, Dec. 2004. [41] R. Shao, W. Hu, Y. Wang, and X. Qi, “The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform,” Measurement, vol. 54, pp. 118–132, Aug. 2014. [42] Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Adv. Adapt. Data Anal., vol. 1, no. 1, pp. 1–41, Jan. 2009. [43] C. Li, J. Valente de Oliveira, M. Cerrada, F. Pacheco, D. Cabrera, V. Sanchez, and G. Zurita, “Observer-biased bearing condition monitoring: From fault detection to multi-fault classification,” Eng. Appl. Artif. Intell., vol. 50, pp. 287–301, Apr. 2016. [44] Y. Chen, X. Pei, S. Nie, and Y. Kang, “Monitoring and Diagnosis for the DC-DC Converter Using the Magnetic Near Field Waveform,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1634–1647, May 2011. [45] M. Robnik-Šikonja and I. Kononenko, “Theoretical and Empirical Analysis of ReliefF and RReliefF,” Mach. Learn., vol. 53, no. 1/2, pp. 23–69, 2003. [46] J. A. Grajales, H. F. Quintero, C. A. Romero, E. Henao, J. F. López, and D. Torres, “Combustion pressure estimation method of a spark ignited combustion engine based on vibration signal processing,” J. Vibroengineering, vol. 18, no. 7, pp. 4237–4247, Nov. 2016. [47] R. Johnsson, “Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals,” Mech. Syst. Signal Process., vol. 20, no. 8, pp. 1923–1940, Nov. 2006. [48] G. Daza-Santacoloma, J. D. Arias-Londono, J. I. Godino-Llorente, N. Sáenz-Lechón, V. Osma-Ru’\iz, and G. Castellanos-Dominguez, “Dynamic feature extraction: an application to voice pathology detection,” Intell. Autom. Soft Comput., vol. 15, no. 4, pp. 667–682, 2009. [49] X. He, D. Cai, and P. Niyogi, “Laplacian score for feature selection,” in Neural Information Processing Systems, NIPS 2005, 2005, vol. 18, p. 189. [50] B. S. Yang, T. Han, and J. L. An, “ART–KOHONEN neural network for fault diagnosis of rotating machinery,” Mech. Syst. Signal Process., vol. 18, no. 3, pp. 645–657, May 2004. [51] J. A. Lee, E. Renard, G. Bernard, P. Dupont, and M. Verleysen, “Type 1 and 2 mixtures of Kullback–Leibler divergences as cost functions in dimensionality reduction based on similarity preservation,” Neurocomputing, vol. 112, pp. 92–108, Jul. 2013. [52] C. Verucchi, G. Bossio, J. Bossio, and G. Acosta, “Fault detection in gear box with induction motors: an experimental study,” IEEE Lat. Am. Trans., vol. 14, no. 6, pp. 2726–2731, Jun. 2016. [53] A. M. Álvarez-Meza, J. A. Lee, M. Verleysen, and G. Castellanos-Domínguez, “Kernel-based dimensionality reduction using Renyi’s α-entropy measures of similarity,” Neurocomputing, vol. 222, pp. 36–46, Jan. 2017.
dc.rightshttps://creativecommons.org/licenses/by/3.0/deed.es_ESen-US
dc.sourceTecnoLógicas; Vol. 20 No. 39 (2017); 157-172en-US
dc.sourceTecnoLógicas; Vol. 20 Núm. 39 (2017); 157-172es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectInternal combustion enginesen-US
dc.subjectvibration signalen-US
dc.subjectmulti-domain featuresen-US
dc.subjectrelevance analysisen-US
dc.subjectfeature selectionen-US
dc.subjectMotores de combustión internaes-ES
dc.subjectseñales de vibraciónes-ES
dc.subjectcaracterísticas multi-dominioes-ES
dc.subjectanálisis de relevanciaes-ES
dc.subjectselección de característicases-ES
dc.titleFeature relevance estimation for vibration-based condition monitoring of an internal combustion engineen-US
dc.titleEstimación de características relevantes para el monitoreo de condición de motores de combustión interna a partir de señales de vibraciónes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem