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

Supplement: "Localization and broadband follow-up of the gravitational-wave transient GW150914" (2016, ApJL, 826, L13)

This Supplement provides supporting material for Abbott et al. (2016a). We briefly summarize past electromagnetic (EM) follow-up efforts as well as the organization and policy of the current EM follow-up program. We compare the four probability sky maps produced for the gravitational-wave transient GW150914, and provide additional details of the EM follow-up observations that were performed in the different bands

Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery

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Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field