Abstract
This research addresses a sensor fault detection and recovery methodology oriented to a real system as can be a geothermal heat exchanger installed as part of the heat pump installation at a bioclimatic house. The main aim is to stablish the procedure to detect the anomaly over a sensor and recover the value when it occurs. Therefore, some experiments applying a Multi-layer Perceptron (MLP) regressor, as modelling technique, have been made with satisfactory results in general terms. The correct election of the input variables is critical to get a robust model, specially, those features based on the sensor values on the previous state.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alaiz Moretón, H., Calvo Rolle, J., García, I., Alonso Alvarez, A.: Formalization and practical implementation of a conceptual model for PID controller tuning. Asian J. Control 13(6), 773–784 (2011)
Basden, A.G., Atkinson, D., Bharmal, N.A., Bitenc, U., Brangier, M., Buey, T., Butterley, T., Cano, D., Chemla, F., Clark, P., Cohen, M., Conan, J.M., de Cos, F.J., Dickson, C., Dipper, N.A., Dunlop, C.N., Feautrier, P., Fusco, T., Gach, J.L., Gendron, E., Geng, D., Goodsell, S.J., Gratadour, D., Greenaway, A.H., Guesalaga, A., Guzman, C.D., Henry, D., Holck, D., Hubert, Z., Huet, J.M., Kellerer, A., Kulcsar, C., Laporte, P., Le Roux, B., Looker, N., Longmore, A.J., Marteaud, M., Martin, O., Meimon, S., Morel, C., Morris, T.J., Myers, R.M., Osborn, J., Perret, D., Petit, C., Raynaud, H., Reeves, A.P., Rousset, G., Sanchez Lasheras, F., Sanchez Rodriguez, M., Santos, J.D., Sevin, A., Sivo, G., Stadler, E., Stobie, B., Talbot, G., Todd, S., Vidal, F., Younger, E.J.: Experience with wavefront sensor and deformable mirror interfaces for wide-field adaptive optics systems. Mon. Not. R. Astron. Soc. 459(2), 1350–1359 (2016). https://doi.org/10.1093/mnras/stw730
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., Varoquaux, G.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)
Cabrerizo, J.A.R., Santos, M.: ParaTrough: modelica-based simulation library for solar thermal plants. Rev. Iberoam. Autom. Inform. Ind. RIAI 14(4), 412–423 (2017). http://www.sciencedirect.com/science/article/pii/S1697791217300481
Calvo-Rolle, J.L., Casteleiro-Roca, J.L., Quintián, H., del Carmen Meizoso-Lopez, M.: A hybrid intelligent system for PID controller using in a steel rolling process. Expert Syst. Appl. 40(13), 5188–5196 (2013). http://www.sciencedirect.com/science/article/pii/S0957417413001632
Calvo-Rolle, J.L., Fontenla-Romero, O., Pérez-Sánchez, B., Guijarro-Berdinas, B.: Adaptive inverse control using an online learning algorithm for neural networks. Informatica 25(3), 401–414 (2014). http://www.mii.lt/informatica/htm/INFO1028.htm
Calvo-Rolle, J.L., Quintian-Pardo, H., Corchado, E., del Carmen Meizoso-López, M., García, R.F.: Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load. J. Appl. Logic 13(1), 37–47 (2015)
Casteleiro-Roca, J., Calvo-Rolle, J., Meizoso-Lpez, M., Pin-Pazos, A., Rodrguez-Gmez, B.: Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump. Neurocomputing 150, Part A, 90–98 (2015). http://www.sciencedirect.com/science/article/pii/S0925231214012417
Casteleiro-Roca, J.L., Calvo-Rolle, J.L., Méndez Pérez, J.A., Roqueñí Gutiérrez, N., de Cos Juez, F.J.: Hybrid intelligent system to perform fault detection on bis sensor during surgeries. Sensors 17(1), 179 (2017)
Casteleiro-Roca, J.L., Jove, E., Sánchez-Lasheras, F., Méndez-Pérez, J.A., Calvo-Rolle, J.L., de Cos Juez, F.J.: Power cell SOC modelling for intelligent virtual sensor implementation. J. Sens. 2017, 10 (2017)
Casteleiro-Roca, J.L., Quintián, H., Calvo-Rolle, J.L., Corchado, E., del Carmen Meizoso-López, M., Piñón-Pazos, A.: An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger. J. Appl. Logic 17, 36–47 (2016)
Crespo-Ramos, M.J., Machn-Gonzlez, I., Lpez-Garca, H., Calvo-Rolle, J.L.: Detection of locally relevant variables using SOMNG algorithm. Eng. Appl. Artif. Intell. 26(8), 1992–2000 (2013). http://www.sciencedirect.com/science/article/pii/S095219761300078X
Scikit-learn Developers: scikit-learn v0.19.1 (2017). http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
Fernández-Serantes, L.A., Vázquez, R.E., Casteleiro-Roca, J.L., Calvo-Rolle, J.L., Corchado, E.: Hybrid intelligent model to predict the SOC of a LFP power cell type. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 561–572. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07617-1_49
Fernndez, J.A., Muiz, C.D., Nieto, P.G., de Cos Juez, F., Lasheras, F.S., Roque, M.: Forecasting the cyanotoxins presence in fresh waters: a new model based on genetic algorithms combined with the MARS technique. Ecol. Eng. 53, 68–78 (2013). http://www.sciencedirect.com/science/article/pii/S0925857412003692
Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems. O’Reilly Media (2017). https://books.google.es/books?id=I6qkDAEACAAJ
Gonzalez-Cava, J.M., et al.: A machine learning based system for analgesic drug delivery. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds.) SOCO/CISIS/ICEUTE -2017. AISC, vol. 649, pp. 461–470. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67180-2_45
Jove, E., Blanco-Rodríguez, P., Casteleiro-Roca, J.L., Moreno-Arboleda, J., López-Vázquez, J.A., de Cos Juez, F.J., Calvo-Rolle, J.L.: Attempts prediction by missing data imputation in engineering degree. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds.) SOCO/CISIS/ICEUTE -2017. AISC, vol. 649, pp. 167–176. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67180-2_16
Jove, E., Gonzalez-Cava, J.M., Casteleiro-Roca, J.L., Pérez, J.A.M., Calvo-Rolle, J.L., de Cos Juez, F.J.: An intelligent model to predict ANI in patients undergoing general anesthesia. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds.) SOCO/CISIS/ICEUTE -2017. AISC, vol. 649, pp. 492–501. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67180-2_48
Juez, F.J., Lasheras, F.S., Roque, N., Osborn, J.: An ANN-based smart tomographic reconstructor in a dynamic environment. Sensors 12(7), 8895–8911 (2012). http://www.mdpi.com/1424-8220/12/7/8895
Quintián, H., Calvo-Rolle, J.L., Corchado, E.: A hybrid regression system based on local models for solar energy prediction. Informatica 25(2), 265–282 (2014). http://www.mii.lt/informatica/htm/INFO1024.htm
Quintian Pardo, H., Calvo Rolle, J.L., Fontenla Romero, O.: Application of a low cost commercial robot in tasks of tracking of objects. Dyna 79(175), 24–33 (2012)
Rolle, J., Gonzalez, I., Garcia, H.: Neuro-robust controller for non-linear systems. Dyna 86(3), 308–317 (2011)
Ruder, S.: An overview of gradient descent optimization algorithms, pp. 1–14 (2016). http://arxiv.org/abs/1609.04747
Tuv, E.: Feature selection with ensembles, artificial variables, and redundancy elimination. J. Mach. Learn. Res. 10, 1341–1366 (2009). http://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf
Vilar-Martinez, X.M., Montero-Sousa, J.A., Calvo-Rolle, J.L., Casteleiro-Roca, J.L.: Expert system development to assist on the verification of “TACAN” system performance. Dyna 89(1), 112–121 (2014)
Zotes, F.A., Peas, M.S.: Heuristic optimization of interplanetary trajectories in aerospace missions. Rev. Iberoam. Autom. Inform. Ind. RIAI 14(1), 1–15 (2017). http://www.sciencedirect.com/science/article/pii/S1697791216300486
Acknowledgments
We would like to thank the ‘Instituto Enerxético de Galicia’ (INEGA) and ‘Parque Eólico Experimental de Sotavento’ (Sotavento Foundation) for their technical support on this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Alaiz-Moretón, H., Casteleiro-Roca, J.L., Robles, L.F., Jove, E., Castejón-Limas, M., Calvo-Rolle, J.L. (2018). Sensor Fault Detection and Recovery Methodology for a Geothermal Heat Exchanger. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)