Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Neuro-Fuzzy for Sensor Fault Detection and Isolation

Print
PDF
 
Download Full Text
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 90 - Number 17
Year of Publication: 2014
Authors:
Rajendra Sharma
Snehal Kokil
Priti Khaire
10.5120/15816-4704

Rajendra Sharma, Snehal Kokil and Priti Khaire. Article: Neuro-Fuzzy for Sensor Fault Detection and Isolation. International Journal of Computer Applications 90(17):42-46, March 2014. Full text available. BibTeX

@article{key:article,
	author = {Rajendra Sharma and Snehal Kokil and Priti Khaire},
	title = {Article: Neuro-Fuzzy for Sensor Fault Detection and Isolation},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {90},
	number = {17},
	pages = {42-46},
	month = {March},
	note = {Full text available}
}

Abstract

This paper presents the sensor fault configuration through Neuro-Fuzzy. As we know that sensor faults have been observed in may domain. Various sensors faults are present such as bias, scaling, drift so to remove this kind of fault which is present we make the sensor to reconfigure to normal condition and this reconfiguration is done through Neuro-Fuzzy which uses the expert knowledge stored in them while training. This technique is implemented through ANFIS tool. Sugeno-Type fuzzy inference system is used, which is adaptive in nature and also Gaussian membership function is used. This technique uses the hybrid optimization which consists of combination of backpropagation and least square method algorithm. Simulation result is shown.

References

  • Girishkumar sago(2008),"Pilot in Loop Assessment of Fault Tolerance Flight Control System",p1
  • L. Fortuna, G. Rizzotta, M. Lavorgna, G. Nunnari, M. G. xibilia and R. Caponetto (2011),"Soft Computing",Springer, p. 167-173.
  • Luger, George; Stubblefield, William A. (2004) "Artificial Intelligence: Structures and Strategies for Complex Problem Solving ", The Benjamin/Cummings Publishing Company, p227-231
  • Manish Mahajan, RajdevTiwari (2010), "Introduction to Soft Computing", Acme Learning Pvt. Ltd. , p. 9-13, 17-19,165-172.
  • McCorduck, Pamela (2004) "Machines Who Think", Natick, MA: A. K. Peters, Ltd. , p. 327–335, 434–435.
  • Nilsson, Nils (1998) "Artificial Intelligence: A New Synthesis", Morgan Kaufmann Publishers. p174
  • R. A Edmunds. (1988) "The Prentice Hall Guide to Expert Systems", Prentice Hall, Englewood,p. 130
  • Robert J. Schalkoff (2011), "Artificial Neural Networks", TATA McGRAW-HILL, p-360-380.
  • Russell, Stuart J. ; Norvig, Peter (2003), "Artificial Intelligence: A Modern Approach" , Upper Saddle River, New Jersey: Prentice Hall, p(22-24)
  • S. N. Sivanandam, M. Paulraj (2011), "Introduction to artificial neural networks", Vikas Publication House Pvt. Ltd. , p. 8-9.
  • Li Jiang (2011). Sensor fault detection and isolation using system dynamics. PhD thesis, University of Michigan.
  • Patton, R. and Chen, J. (1997). Observer-based fault detection and isolation: robustness and applications. Control Engineering Practice, 5(5):671{82.
  • Mengshoel, O. , Darwiche, A. , and Uckun, S. (2008). Sensor validation using Bayesian networks. In the 9th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Los Angelo's, CA.
  • Park, S. and Lee, C. (1993). Fusion-based sensor fault detection. In Proceedings. IEEE International Symposium on Intelligent Control (Cat. No. 93CH32789), pages 156{61, Chicago, IL, USA. IEEE.
  • Pearson, K. (1901). Lines and planes of closest fit. Philosophical Magazine, 2:559{572.
  • Xu, L. , Oja, E. , and Suen, C. (1992). Modified hebbian learning for curve and surface fitting. Neural Networks, 5(3):441{57.
  • Rojas-Guzman, C. and Kramer, M. (1993). Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes. Engineering Applications of Artificial Intelligence, 6(3):191{202.
  • Mengshoel, O. , Darwiche, A. , and Uckun, S. (2008). Sensor validation using Bayesian Networks. In the 9th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Los Angelo's, CA
  • http://edition. cnn. com/2012/07/05/world/europe/france-air-crash-report
  • http://www. globalspec. com/reference/
  • http://info. iet. unipi. it/~lazzerini/icse/FLToolbox_Estratto2. pdf
  • http://www. macrosensors. com/lvdt_tutorial. html
  • http://www. mycockpit. org/forums/content/378-force-sensors. html
  • http://www. ni. com/cms/images/devzone/tut/c/cb971a32991. gif
  • http://nktechnologies. com/current-sensing. html
  • http://nktechnologies. com/current-sensing. html
  • http://www. omega. com/techref/