Scientific articles
\
Applied sciences. Medicine. Technology
\
Engineering. Technology in general
\
Mechanical engineering in general. Nuclear technology. Electrical engineering. Machinery
\
Electrical engineering
Artificial neural networks based approach for predicting LVDT output characteristics
Journal: International Journal of Engineering and Manufacturing @ijem
Article in issue: 4 vol.8, 2018.
Free access
This paper presents a novel approach for training and output prediction of data of a Linear variable differential transformer (LVDT). LVDT is a commonly used device used in laboratories for measuring linear displacements in specific situations. This article considers application of Artificial Neural Networks (ANNs) for learning and output estimation of LVDT. Real-time experiments were conducted and results were collected for training of ANNs. The Regression results and outputs verified the learning and prediction capability of ANNs.
Artificial neural network
\ LVDT
\ Matlab
\ Simulink
\ Mean square error
\ Regression
Similar articles in the section Electrical engineering
Short address: https://sciup.org/15015853
IDS: 15015853 | DOI: 10.5815/ijem.2018.04.03
References Artificial neural networks based approach for predicting LVDT output characteristics
- Yu D, Ham S, Park J, Yun S. (2011). Analysis and design of LVDT. In Proceedings of IEEE 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 23-26 Nov 2011, Incheon, South Korea, doi: 10.1109/URAI.2011.6146035.
- Masi A, Danisi A, Losito R, Mastino M, Spiezia G. (2010). Study of magnetic interference on a LVDT prototype. In Proceedings of IEEE International Conference on Instrumentation and Measurement Technology, 3-6 May 2010, Austin, USA, doi: 10.1109/IMTC.2010.5488091.
- Santhosh K.V., Roy B.K. (2012). An intelligent displacement technique using LVDT with an optimized ANN. Artificial Intelligent Systems and Machine learning, 4(8): 488-493.
- Joshi S, Harle S.M. (2017). Linear variable differential transducer (LVDT) & its application in Civil Engineering. International Journal of Transportation Engineering and Technology, 3(4): 62-66, doi: 10.11648/ijet.20170304.13.
- Masi A, Danzeca S, Losito R, Peronnard S.R., Spiezia G. (2014). A high precision radiation-tolerant LVDT conditioning module. Nuclear Instruments and Methods in Physics, 745: 73-81, doi: 10.1016/j.nima.2014.01.054.
- Santhosh K.V., Roy B.K. (2012). A smart displacement measuring technique using Linear variable displacement transducer. Procedia Technology, 4(2012): 854-861, doi: 10.1016/j.protcy.2012.05.140.
- Liu Y.T., Kuo Y.L., Yan D.W. (2017). System integration for on-machine measurement using a capacitive LVDT-like contact sensor. Advances in Manufacturing, 5(1): 50-58, doi: 10.1007/s40436-016-0169-y.
- Meydan T, Healey G.W. (1992). Linear variable differential transformer (LVDT): linear displacement transducer utilizing ferromagnetic amorphous metallic glass ribbons. Sensors and Actuators, 32(1-3): 582-587, doi: 10.1016/0924-4247(92)80047-7.
- Tian G.Y., Zhao Z.X., Bainer R.W., Zhang N. (1997). Computational algorithms for linear variable differential transformers (LVDTs). IEE Proceedings-Science, Measurement and Technology, 144(4): 189-192, doi: 10.1049/ip-smt:19971262.
- Muhammad U.K., Umar S. (2013). Sensitivity determination of Linear variable differential transducer (LVDT) in fluid level detection techniques. International Journal of Modern Engineering Sciences, 2(2): 73-83.
- Anandan N., George B. (2017). Design and development of a planar linear variable differential transformer for displacement sensing. IEEE Sensors Journals, 17(16): 5298-5305, doi: 10.1109/JSEN.2017.271.9101.
- Suresh V., Ramu G. (2012). Real time measurement of position as well as direction using linear variable differential transformer. International Journal of Engineering Research and Applications, 2(3): 246-252.
- Uhrig R.E. (1995). Introduction to artificial neural networks. In Proceedings of 21st IEEE International Conference on Industrial Electronics, Control and Instrumentation (IECON), 6-10 Nov 1995, Orlando, USA, doi: 10.1109/IECON.1995.483329.
- Bose B.K. (2001). Neural network applications in power electronics and motor drives-An introduction and perspective. IEEE Transaction on Industrial Electronics, 54(1): 14-33, doi: 10.1109/TIE.2006.888683.
- Shi L., Wang X.C. (2010). Artificial neural networks: current applications in modern medicine. In Proceedings of IEEE International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE), 12-13 June 2010, Chengdu, China, doi: 10.1109/CCTAE.2010.5543470.
- Bhuvaneswari S., & Sabarathinam, J. (2013). Defect analysis using artificial neural network. International Journal of Intelligent Systems and Applications, 6(5): 33-38, doi: 10.5815/ijisa.2013.05.05.
- Al-Maqaleh B.M., Al-Mansoub A.A., & Al-Badani F.N. (2016). Forecasting using Artificial neural network and statistics models. International Journal of Education and Management Engineering, 6(3): 20-32, doi:10.5815/ijeme.2016.03.03.
- Kumar K., & Abhishek (2012). Artificial neural network for diagnosis of kidney stone disease. International Journal of Information Technology and Computer Science, 5(7): 20-25, doi: 10.5815/ijitcs.2012.07.03.
- Duranton M. (1996). Image processing by neural networks. IEEE Micro, 16(5): 12-19, doi: 10.1109/40.540076.
- Baptista, D., & Morgad-Dias, F. (2013). A Survey of artificial neural network training tools. Neural Computing and Applications. 23(3-4):609-615, doi: 10.1007/s00521-013-1408-9.
- Liu H, Cocea M. (2017). Semi-random partitioning of data into training and test sets in granular computing context. Granular Computing, 2(4): 357-386, doi: 10.1007/s41066-017-0049.
- Jayne C, IIiads L, Mladenov V. (2016). Special issue on the engineering applications of neural networks. Neural Computing and Applications. 27(5): 1075-1076, doi: 10.1007/s00521-016-2318-4.
- Zhao S.J., Xu Y.M. (2004). Levenberg-Marquardt algorithm for nonlinear principal component analysis neural network through inputs training. In Proceeding of IEEE Fifth World Congress on Intelligent Control and Automation, 15-19 June 2004, Hangzhou, China, doi: 10.1109/WCICA.2004.1343139.
- Dao-de S. (2000). Selection of the linear regression model according to the parameter estimation. Wuhan University Journal of Natural Science, 5(4): 400-405, doi: 10.1007.BF0285076.
