ANOVA Method Applied to PEMFC Ageing Forecasting Using an Echo State Network

International audienceAccording to the International Energy Agency, an increase of the requests of energy of 40% could arise in the next decades, mainly due to the emergence of developing countries. The problem with the nowaday energy system is the use of fossil energy, which is limited and attempt to disappear in the near future. Thus an energy transition has to begin in order to replace the fossil fuels and anticipate their disappearance. Consequently, in recent years, the promotion and development of renewable energy have been realized. One of this renewable energy, the energy vector hydrogen, appears to be a promising solution, mainly due to interesting performance of Fuel Cells (FC) systems and hydrogen abundance on Earth (it is still important to underline that the hydrogen does not exist in natural form). However, this research area is still subject to scientific and technological bottlenecks. One of these major bottlenecks preventing the industrialization of FC systems is it limited useful lifetime. It is therefore important to develop reliable tools for the diagnosis and prognosis of FC system in order to optimize its efficiency. The aim of this article is to present the results of a sensibility analysis applied to a prognosis tools called Echo State Network

Predicting the Remaining Useful Lifetime of a Proton Exchange Membrane Fuel Cell using an Echo State Network

International audienceOne remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it's important to develop failure diagnostic and prognostic tools enabling the optimization of FC. The Prognostic and Heath Management (PHM) is a discipline involved in the process of industrial maintenance. The objective, in PHM, is to estimate the Remaining Useful Life (RUL) of a system by predicting its future behavior. The RUL enables to predict the moment when a fault could occur on a system. It also allows identifying the relevant part of the system where a fault could happen. Then, a preventive maintenance could be performed to avoid non-reversible degradations. Three main prognosis approaches can be distinguished: model-based, data-based and hybrid methods. Data-based methods such as Artificial Neural Network (ANN), aim to estimate the ageing behavior of the process without specific knowledges related to the physical system phenomenon. Nevertheless, the deployment of such an approach can be a tedious work, mainly due to the trial and error algorithm method, which represents a real problem for industrial applications where real-time complying algorithms must be developed. Among the various methods of this area, the tool chosen here is called Echo State Network (ESN). An ESN consists in the use of a dynamical neurons reservoir where the training step consists in performing a linear regression. The computation time of this algorithm is thus shorter while keeping the same modeling capability of a Recurrent Neural Network (RNN). Created in 2001 by H. Jaeger, an ESN proposes a better human brain paradigm than traditional ANN, and are based on a reservoir of neurons randomly connected to each other. The aim of this paper is to study the application of ESN as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell using an iterative predictive structure, which is the most common approach performing a one-step prediction. This estimation output value is used in the next step as one of the input regressor and these operations can be repeated until the desired prediction horizon. The results obtained thanks to this method exhibits good prediction and they will be detailed in this paper

Fuel Cells prognostics using Echo State Network

International audienceOne remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it is important to develop failure diagnostic and prognostic tools enabling the optimization of the FC. Among all the existing prognostics approaches, datamining methods such as artificial neural networks aim at estimating the process' behavior without huge knowledge about the underlying physical phenomena. Nevertheless, this kind of approach needs huge learning dataset. Also, the deployment of such an approach can be long (trial and error method), which represents a real problem for industrial applications where realtime complying algorithms must be developed. According to this, the aim of this paper is to study the application of a reservoir computing tool (the Echo State Network) as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell. Developments emphasize on the prediction of the mean voltage cells of a degrading FC. Accuracy and time consumption of the approach are studied, as well as sensitivity of several parameters of the ESN. Results appear to be very promising

Artificial Intelligence and Industry 4.0

Tato bakalářská práce se zabývá rešerší používanými metodami umělé inteligence v diagnostiky technických soustav. První část práce je věnována rešerši používaných metod v oblasti diagnostiky technických soustav. V druhé části je uvedeno shrnutí vlastností a použití těchto metod. Závěr práce se věnuje aplikaci metody umělé neuronové sítě a hybridní metody „adaptivní neuro fuzzy inferenční systém“, jejímž základem je neuronová síť. Tato část se zabývá podrobným popisem a použitím těchto metod v reálných technických soustavách.This bachelor thesis deals with search for artificial intelligence methods used in the diagnostics of technical systems. The first part of the work is devoted to the search of used methods in the field of diagnostics of technical systems. The second part summarizes the characteristics and applications of these methods. The conclusion of the thesis deals with the application of the artifical neural network method and the hybrid method „adaptive neuro-fuzzy inference systém“, which is based on the neural network. This part deals with a detailed description and use of these methods in real technical systems.

Fault diagnosis in industrial process by using LSTM and an elastic net

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature.[ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.Márquez-Vera, MA.; López-Ortega, O.; Ramos-Velasco, LE.; Ortega-Mendoza, RM.; Fernández-Neri, BJ.; Zúñiga-Peña, NS. (2021). Diagnóstico de fallas mediante una LSTM y una red elástica. Revista Iberoamericana de Automática e Informática industrial. 18(2):164-175. https://doi.org/10.4995/riai.2020.13611OJS164175182Adewole, A., Tzoneva, R., Behardien, S., 2016. Distribution network fault section identification and fault location using wavelet entropy and neural networks. Applied Soft Computing 46, 296-306. https://doi.org/10.1016/j.asoc.2016.05.013Alkaya, A., Eker, I., 2011. Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application. ISA Transactions 50, 287-302. https://doi.org/10.1016/j.isatra.2010.12.004Barakat, S., Eteiba, M., Wahba, W., 2014. Fault location in underground cables using anfis nets and discrete wavelet transform. Journal of Electrical Systems and Information Technology 1, 198-211. https://doi.org/10.1016/j.jesit.2014.12.003Bathelt, A., Ricker, N., Jelali, M., 2015. Revision of the Tennessee Eastman process model. IFAC Papers-Online 48 (8), 309-314. https://doi.org/10.1016/j.ifacol.2015.08.199Boldt, F., Rauber, T., Varejao, F., October 2014. Evaluation of the extreme learning machine for automatic fault diagnosis of the Tennessee Eastman chemical process. In: IEEE (Ed.), Annual Conference of the IEEE Industrial Electronics Society. Vol. 40. Dallas, Texas, pp. 2551-2557. https://doi.org/10.1109/IECON.2014.7048865Chen, H., Tino, P., Yao, X., 2014. Cognitive fault diagnosis in Tennessee Eastman process using learning in the model space. Computers and Chemical Engineering 67, 33-42. https://doi.org/10.1016/j.compchemeng.2014.03.015Rodrigues, J., Filho, P., PeixotoJr., E., Kumar, A., deAlbuquerque, V., 2019. Classification of EEG signals to detect alcoholism using machine learning techniques. Pattern Recognition Letters 125, 140-149. https://doi.org/10.1016/j.patrec.2019.04.019Dixit, A., Majumdar, S., 2013. Comparative analysis of coiflet and daubechies wavelets using global threshold for image denoising. Intenational Journal of Advances in Engineering & Technology 6 (5), 2247-2252.Downs, J., Vogel, E., 1993. A plant-wide industrial process control problem. Computers and Chemical Engineering 17 (3), 245-255. https://doi.org/10.1016/0098-1354(93)80018-IFischer, T., Krauss, C., 2018. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270, 654-669. https://doi.org/10.1016/j.ejor.2017.11.054Gao, X., Hou, J., 2016. An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process. Neurocomputing 174, 906-911. https://doi.org/10.1016/j.neucom.2015.10.018Geng, Z., Li, Z., Han, Y., 2018. A new deep belief network based on RBM with glial chains. Information Sciences 463, 294-306. https://doi.org/10.1016/j.ins.2018.06.043Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. MIT Press, United States of America, http://www.deeplearningbook.ogr.Han, L., Li, C., Guo, S., Su, X., 2015. Feature extraction method of bearing AE signal based on improved Fast-ICA and wavelet packet energy. Mechanical Systems and Signal Processing 62-63, 91-99. https://doi.org/10.1016/j.ymssp.2015.03.009Hastie, T., Tibshirani, R., Friedman, J., 2009. The elements of statistical learning: data mining, inference and prediction. Springer, New York. https://doi.org/10.1007/978-0-387-84858-7Hoang, D., Kang, H., 2019. A survey on deep learning based bearing fault diagnosis. Neurocomputing 335, 327-335. ttps://doi.org/10.1016/j.neucom.2018.06.078Hochreiter, S., Schmidhuber, J., 1997. Long short term memory. Neural Computation 9 (8), 1735-1780. ttps://doi.org/10.1162/neco.1997.9.8.1735Hyvärinen, A., Oja, E., 2000. Independent component analysis: Algorithms and applications. Neural Networks 13, 411-430. ttps://doi.org/10.1016/S0893-6080(00)00026-5Jing, C., Gao, X., Zhu, X., Lang, S., July 2014. Fault classificaction on Tennessee Eastman process: PCA and SVM. In: IEEE (Ed.), Intenational Conference on Mecatronics and Control. Jinzhou, China, pp. 2194-2197. https://doi.org/10.1109/ICMC.2014.7231958Jung, C., Kim, K., Lee, J., Klockl, B., 2007. Wavelet and neuro-fuzzy based fault location for combined transmission systems. Energy Systems 29, 445-454. https://doi.org/10.1016/j.ijepes.2006.11.003Kandula, V. K., 2011. Fault detection in process control plants using principal component analysis. Master's thesis, Louisiana State University, Department of Electrical Engineering.Karpenko, M., Sepehri, N., Octubre 2001. A neural network based fault detection and identification scheme for pneumatic process control valves. In: IEEE (Ed.), International Conference on Systems, Man and Cybernetics. Tucson, USA, pp. 93-98. https://doi.org/10.1109/ICSMC.2001.969794Khakipour, M., Safavi, A., Setoodeh, P., 2017. Bearing fault diagnosis with morphological gradient wavelet. Journal of the Franklin Institute 354, 2465-2476. https://doi.org/10.1016/j.jfranklin.2016.11.013Kuang, T., Yang, Z., Yao, Y., 2015. Multivariate fault isolation via variable selection in discriminant analysis. Journal of Process Control 35, 30-40. https://doi.org/10.1016/j.isatra.2017.06.014Kumar, R., Bansal, H., 2019. Hardware in the loop implementation of wavelet based strategy in shuntactive powerfilter to mitigate power quality issues. Electric Power Systems Research 169, 92-104. https://doi.org/10.1016/j.epsr.2019.01.001Lau, C., Ghosh, K., Hussain, M., Hassan, C. C., 2013. Fault disgnosis of Tennessee Eastman process with multi-scale PCA and ANFIS. Chemom. Intell. Lab. Syst. 120, 1-14. https://doi.org/10.1016/j.chemolab.2012.10.005Lee, J., Yoo, C., Lee, I., 2004. Statistical process monitoring with independent component analysis. Journal of Process Control 14 (5), 467-485. https://doi.org/10.1016/j.jprocont.2003.09.004Lei, J., Liu, C., Jiang, D., 2019. Fault diagnosis of wind turbine based on long short-term memory networks. Renewable Energy 133, 422-432. https://doi.org/10.1016/j.renene.2018.10.031Li, W., Monti, A., Ponci, F., 2014. Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks. IEEE Transactions on Instrumentation and Measurement 63 (11), 2651-2665. https://doi.org/10.1109/TIM.2014.2313035Liang, P., Deng, C.,Wu, J., Yang, Z., Zhu, J., Zhang, Z., 2019. Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Computers in Industry 113, 103132. https://doi.org/10.1016/j.compind.2019.103132Lin, J., Zhang, A., 2005. Fault feature separation using wavelet-ICA filter. NDT&E International 38, 421-427. https://doi.org/10.1016/j.ndteint.2004.11.005Linker, R., Gutman, P., Seginer, I., 2002. Observer-based robust failure detection and isolation in greenhouses. Control Engineering Practice 10 (5), 519- 531. https://doi.org/10.1016/S0967-0661(02)00002-3Lou, W., Loparo, K., 2004. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing 18, 1077-1095. https://doi.org/10.1016/S0888-3270(03)00077-3Lv, F.,Wen, C., Bao, Z., Liu, M., 2016. Fault diagnosis based on deep learning. In: AACC (Ed.), American Control Conference. Boston, USA, pp. 6851-6856. https://doi.org/10.1109/ACC.2016.7526751Lv, F., Wen, C., Liu, M., Bao, Z., 2017. Weighted time series fault diagnosis based on a staked sparce autoencoder. Journal of Chemometrics 31, 16 pages. https://doi.org/10.1002/cem.2912Lv, F., Fan, X., Wen, C., Bao, Z., 2018. Stacked sparse auto encoder network based multimode process monitoring. In: IEEE (Ed.), International Conference on Control Automation & Information Science. Hangzhou, China, pp. 227-232. https://doi.org/10.1109/ICCAIS.2018.8570618Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M., 1998. ECG pattern recognition and classification using non-linear transfor mations and neural networks: A review. International Journal of Medical Informatics 52, 191-208. https://doi.org/10.1016/S1386-5056(98)00138-5Methnani, S., Lafont, F., Gautier, J., Damak, T., Toumi, A., 2013. Actuator and sensor fault detection, isolation and identification in nonlinear dynamical systems, with applications to a waste water treatment plant. Journal of Computer Engineering and Informatics 1 (4), 112-125. https://doi.org/10.1080/21642583.2014.888525Muñoz-Cobo, J., Mendizábal, R., Miquel, A., Berna, C., Escrivá, A., 2017. Use of the principles of maximum entropy and maximum relative entropy for the determination of uncertain parameter distributions in engineering applications. Entropy 19, 486, 37 pages. https://doi.org/10.3390/e19090486Nguyen, B., Quyen, A., Nguyen, P., Ton, T., July 2017. Wavelet-based neural network for recognition of faults at nhabe power substation of the vietnam power system. In: IEEE (Ed.), International Conference on System Science and Engineering. Ho Chi Minh City, Vietnam, pp. 165-168. https://doi.org/10.1109/ICSSE.2017.8030858Ojeda-González, A., Mendes-Jr., O., Oliveira-Domingues, M., Menconi, V., 2014. Daubechies wavelet coeffcients: a tool to study interplanetary magnetic field fluctuations. Geof'ısica Internacional 53 (2), 101-115. https://doi.org/10.1016/S0016-7169(14)71494-1Oliveira, J., Pontes, K., Santori, I., Embirucu, M., 2017. Fault detection and diagnosis in dynamic systems using weightless neural networks. Expert Systems With Applications 84, 200-219. https://doi.org/10.1016/j.eswa.2017.05.020Patan, K., 2008. Artificial neural networks for the modelling and fault diagnosis of technical process. Lecture Notes in Control and Information Sciences. Springer, India.Rafiee, J., Rafiee, M., Tse, P., 2010. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 37, 4568-4579. https://doi.org/10.1016/j.eswa.2009.12.051Ramos-Velasco, L., Ramos-Fernández, J., Islar-Gómez, O., Espejel-Rivera, M., García-Lamont, J., Márquez-Vera, M., 2013. Identificación y control wavenet de un motor de ca. Revista Iberoamericana de Automática e Informática Industrial 10, 269-278. https://doi.org/10.1016/j.riai.2013.05.002Rato, T., Reis, M., 2013. Defining the structure of DPCA models and its impact on process monitoring and prediction ctivities. Chemometrics and Intelligent Laboratory Systems 125, 74-86. https://doi.org/10.1016/j.chemolab.2013.03.009Rockinger, M., Jondeau, E., 2002. Entropy densities with an application to autoregressive conditional skewness and kurtosis. Journal of Econometrics 106, 119-142. https://doi.org/10.1016/S0304-4076(01)00092-6Salahschoor, K., Kiasi, F., July 2008. On-line process monitoring based on wavelet-ICA methodology. In: IFAC (Ed.), Proceedings of the 17th World Congress. Seul- Korea, pp. 6-11. https://doi.org/10.3182/20080706-5-KR-1001.01253Salahshoor, K., Khoshro, M., Kordestani, M., 2011. Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems. Simulation Modelling Practice and Theory 19, 1280-1293. https://doi.org/10.1016/j.simpat.2011.01.005Sharif, I., Khare, S., 2014. Comparative analysis of Haar and Daubechies wavelet for hyper spectral image classification. In: Commission, I. T. (Ed.), VIII Symposium of The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science. Hyderabad-India, pp. 937-941. https://doi.org/10.5194/isprsarchives-XL-8-937-2014Smirnov, E., Timoshenko, D., Adrianov, S., 2014. Comparison of regularization methods for imagenet classification with deep convolutional neural networks. AASRI Procedia 6, 89-94. https://doi.org/10.1016/j.aasri.2014.05.013Sobhani-Tehrani, E., Khorasani, K., 2009. Fault diagnosis of nonlinear systems using a hybrid approach. Fault detetion and diagnosis. Springer, Berlin, Ch. 2, pp. 22-49. https://doi.org/10.1007/978-0-387-92907-1_2Tayarani-Bathaie, S., Vanini, Z., Khorasani, K., 2014. Dynamic neural networkbased fault diagnosis of gas turbine engines. Neurocomputing 125, 153-165. https://doi.org/10.1016/j.neucom.2012.06.050Zvokelj, M., Zupan, S., Prebil, I., 2016. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. Journal of Sound and Vibration 26, 394-423. https://doi.org/10.1016/j.jsv.2016.01.046Wang, X., Qin, Y., Wang, Y., Xiang, S., Chen, H., 2019. ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis. Neurocomputing 363, 88-98. https://doi.org/10.1016/j.neucom.2019.07.017Wu, F., Tong, F., Yang, Z., 2016. EMGdi signal enhancement based on ICA decomposition and wavelet transform. Applied Soft Computing 43, 561-571. https://doi.org/10.1016/j.asoc.2016.03.002Wu, J., Hsu, C., Wu, G., 2009. Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. Expert Systems with Applications 36, 6244-6255. https://doi.org/10.1016/j.eswa.2008.07.023Wu, Q., Law, R., Wu, S., 2011. Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm. Expert Systems with Applications 38, 9096-9104. https://doi.org/10.1016/j.eswa.2010.12.109Wu, H., Zhao., Jinsong, 2018. Deep convolutional neural network model based chemical process fault diagnosis. Computers and Chemical Engineering 115, 185-197. https://doi.org/10.1016/j.compchemeng.2018.04.009Xiao, C., Chen, N., Hu, C., Wang, K., Gong, J., Chen, Z., 2019. Short and midterm sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment 233, 111358. https://doi.org/10.1016/j.rse.2019.111358Xie, D., Bai, L., December 2015. A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process. In: IEEE (Ed.), International Conference on Machine Learning and Applications. Vol. 14. Miami, USA, pp. 745-748. https://doi.org/10.1109/ICMLA.2015.208Yan, R., Gao, R., Chen, X., 2014. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 351, 4555-4569. https://doi.org/10.1016/j.sigpro.2013.04.015Yan, Z., Yao, Y., 2015. Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO). Chemometrics and Intelligent Laboratory Systems 146, 136-146. https://doi.org/10.1016/j.chemolab.2015.05.019Yao, G., Lei, T., Zhong, J., 2019. A review of convolutional-neural-networkbased action recognition. Pattern Recognition Letters 118, 14-22. https://doi.org/10.1016/j.patrec.2018.05.018Yin, S., Ding, S., Haghani, A., Hao, H., Zhang, P., 2012. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control 22, 1567-1581. https://doi.org/10.1016/j.jprocont.2012.06.009Zhang, Q., Yang, L., Chen, Z., Li, P., 2018. A survey on deep learning for big data. Information Fusion 42, 146-157. https://doi.org/10.1016/j.inffus.2017.10.006Zhang, X., Polycarpou, M., Parisini, T., 2002. A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems. IEEE Transactions on Automatic Control 47 (4), 576-593. https://doi.org/10.1109/9.995036Zhang, Y., Zhang, L., Zhang, H., 2012. Fault detection for industrial processes. Mathematical Problems in Engineering 2012, 18 pages. https://doi.org/10.1155/2012/757828Zhang, Z., Zhao, J., 2017. A deep belief network based fault diagnosis model for complex chemical process. Computers and Chemical Engineering 107, 395-407. https://doi.org/10.1016/j.compchemeng.2017.02.041Zhao, H., 2018. Neural component analysis for fault detection. Chemometrics and Intelligent Laboratory Systems 176, 11-21. https://doi.org/10.1016/j.chemolab.2018.02.001Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R., 2019. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing 115, 213-237. https://doi.org/10.1016/j.ymssp.2018.05.050Zheng, J., Huang, W., Wang, Z., Liang, J., 2019. Mutual information-based sparse multiblock dissimilarity method for incipient fault detection and diagnosis in plant-wide process. Journal of Process Control 83, 63-76. https://doi.org/10.1016/j.jprocont.2019.09.00