1. Home
  2. Issues
  3. Volume 30, Issue 4 (2019)
  4. Virtual Sensor for Fault Detection, Isol ...

Informatica


Virtual Sensor for Fault Detection, Isolation and Data Recovery for Bicomponent Mixing Machine Monitoring
Volume 30, Issue 4 (2019), pp. 671–687
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 May 2018
Accepted
1 September 2019
Published
1 January 2019

Abstract

The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results.

References

 
Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
 
Carvajal-Godinez, J., Guo, J., Gill, E. (2017). Agent-based algorithm for fault detection and recovery of gyroscope’s drift in small satellite missions. Acta Astronautica, 139, 181–188.
 
Cristianini, N., Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, New York, NY, USA.
 
Fan, H., Wong, C.K., Yuen, M.F. (2006). Prediction of material properties of epoxy materials using molecular dynamic simulation. In: Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems, 2006. EuroSime 7th International Conference on IEEE, pp. 1–4.
 
Fei, C.W., Bai, G.C. (2014). Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression. Mechanical Systems and Signal Processing, 49(1), 196–208.
 
Garg, L., Mcclean, S., Meenan, B., Millard, P. (2011). Phase-type survival trees and mixed distribution survival trees for clustering patients’ hospital length of stay. Informatica, 22(1), 57–72.
 
González, S., Sedano, J., Villar, J.R., Corchado, E., Herrero, Á., Baruque, B. (2015). Features and models for human activity recognition. Neurocomputing, 167, 52–60.
 
Guo, Y., Li, X., Bai, G., Ma, J. (2012). Time series prediction method based on LS-SVR with modified gaussian RBF. In: Huang, T., Zeng, Z., Li, C., Leung, C. (Eds.), Neural Information Processing, Lecture Notes in Computer Science, Vol. 7664. Springer, Berlin, Heidelberg, pp. 9–17.
 
Heredia, G., Ollero, A. (2010). Virtual sensor for failure detection, identification and recovery in the transition phase of a morphing aircraft. Sensors, 10(3), 2188–2201.
 
Hu, J., Lin, H., Li, X., Jiang, C., Qiu, X., Li, W. (2014). State-of-charge estimation for battery management system using optimized support vector machine for regression. Journal of Power Sources, 269(Supplement C), 682–693.
 
Hwang, I., Kim, S., Kim, Y., Seah, C.E. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18(3), 636–653.
 
Jain, A.K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition, 31(8), 651–666.
 
Kaski, S., Sinkkonen, J., Klami, A. (2005). Discriminative clustering. Neurocomputing., 69(13), 18–41.
 
Naidu, Z.R., Zafiriou, E., McAvoy, T.J. (1990). Use of neural networks for sensor failure detection in a control system. IEEE Control Systems Magazine, 10, 49–55.
 
Nicholson, T. (2017). Optimization in Industry: Vol. 1. Optimization Techniques. Routledge.
 
Pal, N., Biswas, J. (1997). Cluster validation using graph theoretic concepts. Pattern Recognition, 30(6), 847–857.
 
Parmee, I., Hajela, P. (2012). Optimization in industry. Springer Science & Business Media.
 
Pinzón, C., de Paz, J.F., Bajo, J., Herrero, Á., Corchado, E. (2010). AIIDA-SQL: an adaptive intelligent intrusion detector agent for detecting SQL injection attacks. In: 10th International Conference on Hybrid Intelligent Systems, pp, pp. 23–25. 73–78.
 
Qi, Z., Tian, Y., Shi, Y. (2013). Robust twin support vector machine for pattern classification. Pattern Recognition, 46(1), 305–316.
 
Qin, A., Suganthan, P. (2005). Enhanced neural gas network for prototype-based clustering. Pattern Recognition, 38(8), 1275–1288.
 
Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113, 130503.
 
Sánchez, R., Herrero, Á., Corchado, E. (2013). Visualization and clustering for SNMP intrusion detection. Cybernetics and Systems, 44(6–7), 505–532.
 
Sharma, A.B., Golubchik, L., Govindan, R. (2010). Sensor faults: detection methods and prevalence in real-world datasets. ACM Transactions on Sensor Networks (TOSN), 6(3), 23.
 
Shen, H., Wu, Z.-G., Park, J.H. (2015). Reliable mixed passive and filtering for semi-markov jump systems with randomly occurring uncertainties and sensor failures. International Journal of Robust and Nonlinear Control, 25(17), 3231–3251.
 
Steinwart, I., Christmann, A. (2008). Support Vector Machines. 1st ed. Springer Publishing Company, Incorporated.
 
Suykens, J., Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.
 
Wang, S., Cui, J. (2005). Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method. Applied Energy, 82(3), 197–213.
 
Wang, L., Wu, J. (2012). Neural network ensemble model using PPR and LS-SVR for stock market forecasting. In: Huang, D.-S., Gan, Y., Bevilacqua, V., Figueroa, J. (Eds.), Advanced Intelligent Computing. Lecture Notes in Computer Science, Vol. 6838. Springer, Berlin, Heidelberg, pp. 1–8.
 
Wang, R., Wang, A., Song, Q. (2012). Research on the alkalinity of sintering process based on LS-SVM algorithms. In: Jin, D., Lin, S. (Eds.), Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, Vol. 168. Springer, Berlin, Heidelberg, pp. 449–454.
 
Wasserman, P. (1993). Advanced Methods in Neural Computing. 1st ed. John Wiley & Sons, Inc., New York, NY, USA.
 
Ye, J., Xiong, T. (2007). Svm versus least squares SVM. Journal of Machine Learning Research – Proceedings Track, 2, 644–651.
 
Zeng, Z., Wang, J. (2010). Advances in Neural Network Research and Applications 1st ed. Springer Publishing Company, Incorporated.

Biographies

Jove Esteban
esteban.jove@udc.es

E. Jove received a MS degree in industrial engineering from the University of Leon in 2014. After working in the automotive industry for two years, he joined the University of A Coruña, Spain, where he is a professor of power electronics in the Faculty of Engineering since 2016. He is a PhD student in the University of La Laguna and his research has been focused on the use of intelligent techniques for nonlinear systems modelling.

Casteleiro-Roca José-Luis
jose.luis.casteleiro@udc.es

J.-L. Casteleiro-Roca received a BS degree from the University of A Coruña in 2003, a MS degree in industrial engineering from the University of Leon in 2012, and currently he is a PhD student in the University of La Laguna. He is a technical engineer in the Spanish Navy since 2004 and, since 2014, he is also part of the teaching and research staff of the UDC as a part-time associate professor. His main lines of research are focused on applying expert system technologies to the diagnosis and control systems and to intelligent systems for control engineering and optimization.

Quintián Héctor
hector.quintian@udc.es

H. Quintián received a MS degree in industrial engineering from the University of Leon in 2010, and a PhD in computer engineering from the University of Salamanca in 2017 (FPU grant). He is a professor of automatic control, Faculty of Engineering, University of A Coruña, Spain. His research efforts have been geared towards artificial intelligence, supervised and unsupervised learning and the training of intelligent systems for control engineering, optimization and education. He has participated in 3 European and 3 National projects. He is the author and co-author of 22 papers of JCR-indexed journals, 22 papers published in international conferences and the co-organizer of more than 15 international conferences (LNAI-AISC-LNCS Springer proceedings).

Méndez-Pérez Juan-Albino
jamendez@ull.edu.es

J.-A. Méndez-Pérez is a full professor at the University of La Laguna since 1993 in the Department of Computer Science and Systems Engineering. His teaching activity has been focused on system modelling and control. He works on lines of research related to control engineering: fuzzy control, predictive control and control applications. He earned a PhD degree in 1998 which had been recognized and awarded with the title of the best PhD Thesis in the field of engineering. He has participated in 25 research projects. He was the principal investigator in 3 of them.

Calvo-Rolle José Luis
jlcalvo@udc.es

J.-L. Calvo Rolle received MS and PhD degrees in industrial engineering from the University of Leon in 2004 and 2007, respectively. He is an associate professor of automatic control and the head of the Industrial Engineering Department, Faculty of Engineering, University of A Coruña, Spain. His main research areas are associated with the application of expert system technologies in diagnosis and control systems and in intelligent training systems for control engineering, optimization and education.


Full article Related articles Cited by PDF XML
Full article Related articles Cited by PDF XML

Copyright
© 2019 Vilnius University
Open access article under the CC BY license.

Keywords
virtual sensor fault detection recovery FDD FDR

Metrics
since January 2020
1760

Article info
views

655

Full article
views

749

PDF
downloads

235

XML
downloads


Share


RSS