Application of a PCA-based fault detection and diagnosis method in a power generation system with a 2 MW natural gas engine
Abstract
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.
Downloads
References
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
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
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
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
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
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
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
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
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
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
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
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
Cardenas, Y. (2019). Fallas en bujías para motores de generación a gas. (Tesis de maestría). Universidad del Atantico.
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
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
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
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
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.
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
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
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
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
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
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
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.
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
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
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
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
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
Zumoffen, D. (2008). Desarrollo de Sistemas de Diagnóstico de Fallas Integrado al Diseño de Control Tolerante a Fallas en Procesos Químicos.
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
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.
Copyright (c) 2022 Yulineth Cardenas, Gaylord Carrillo, Anibal Alviz, Antistio Alviz, Ivan Portnoy, Juan Fajardo, Eric Ocampo, Edson Da-Costa
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.