Survey on Fault Detection and Diagnosis Using Neural Network in WBAN

  • Authors

    • R N.S.Kalpana
    • Dr P.Nallathai
    2018-04-18
    https://doi.org/10.14419/ijet.v7i2.20.16731
  • WBAN, Neural Network, fault diagnosis and fault detection
  • Wireless Body Area Networks (WBAN) is the sensor network used for monitoring health information in e-health systems. WBAN is a combination of sensors used to obtain vital information from the body. It is a special type of WSN. WBAN technology should handle the data in a smart way by reacting to the monitored data and to evaluate the data.  It  requires  fault detection and diagnosis methods for sensors used in WBAN.  This paper gives a survey of different types of neural-network approaches for faults detection and diagnosis in WBAN using neural network.

     

     

  • References

    1. [1] F. Akyildiz, Weilian Su, Yogesh Sankara subramaniam, and Erdal Cayirci.: A survey on sensor networks. IEEE communications magazine 40, 8 102-114. DOI: 10.1109/MCOM.2002.1024422. (2002).

      [2] Chris Otto, Aleksandar Milenkovic, Corey Sanders : System architecture of a wireless body area sensor network for ubiquitous health monitoring. Journal of mobile multimedia 1, 4 (2006).

      [3] N Alrajei, G Corser, H Fu, Y Zhu : “Energy Prediction Based Intrusion Detection in Wireless Sensor Networksâ€. International Journal of Emerging Technology and Advanced Engineering 2014 .

      [4] Marwa Salayma, Ahmed Al-Dubai and Imed Romdhani, Edinburgh Napier University Youssef Nasser, American University of Beirut. Wireless Body Area Network (WBAN): ‘A survey on reliability, fault tolerance, and technologies coexistence’ACM 1544-3558. .2016.

      [5] Karthic.k Raghunath and Rengarajan : “A Systematically Survey Investigation of Faults, Errors and Failures in Wireless Sensor Network “. International Journal of Advanced Computer Research (2013)_

      [6] Arunanshu Mahapatro.: Online fault detection and recovery in body sensor networks’. In Proceedings of the World Congress on Information and Communication Technologies (WICT), IEEE, 407-412. DOI: 10.1109/WICT.2011.6141280. 2011

      [7] B. Sharma, L. Golubchik, and R. Govindan “Sensor Faults: Detection Methods and Prevalence in Real-World Datasets,â€, ACM Transactions on Sensor Networks, vol. 6, no. 3, pp. 1–39, 2010.

      [8] D. Giansanti, M. Dozza,:“Energetic assessment of trunk postural modifications induced by a wearable audio-biofeedback system,†Medical engineering & physics, vol. 31, no. 1, pp. 48–54, 2009.

      [9] C. Wall, D. M. Wrisley “Vibrotactile tilt feedback improves dynamic gait index: a fall risk indicator in older adults, “Gait & posture, vol. 30,no. 1. 16–21, 2009.

      [10] C. Fritz and J. Poitevineau :“Influence of vibrotactile feedback on some perceptual features of violins,â€. The Journal of the Acoustical Society of America, vol. 136, no. 2, pp. 910–921, 2014.

      [11] Fault Detection and Diagnosis in a Bayesian Network classifier incorporating probabilistic IFAC-Papers On Line 670–675,(2015).

      [12] Mohamed Amine A to Sylvain Verron _ Abdessamad Kobi .‘‘Fault Detection and Diagnosis in a Bayesian Network classifier incorporating probabilistic boundary’: IFAC-Papers On Line 48-21 670–676 ,(2015).

      [13] Adriana A. Alexandra: Using Expert Systems for Fault Detection and Diagnosis in Industrial Applications Research Institute for Informatics, Bucharest,.

      [14] Osman Salem, Alexey Guerassimov and Ahmed Mehaoua :Sensor Fault and Patient Anomaly Detection and Classification in Medical Wireless Sensor Networks. IEEE ICC 2013 - Selected Areas in Communications Symposium.

      [15] Y. Yao, A. Sharma, L. Golubchik, and R. GovindaN: “Online Anomaly Detection for Sensor Systems: a Simple and Efficient Approach†n, Performance Evaluation, vol. 67, no. 11, pp. 1059–1075, 2010.

      [16] B. Sharma, L. Golubchik , and R. Govindan :“Sensor Faults: Detection Methods and Prevalence in Real-World Datasets,â€, ACM Transactions on Sensor Networks, vol. 6, no. 3, pp. 1–39, 2010.

      [17] A.Jabbari. W.Lang : “Applications of computer intelligence for sensor fault detection and isolation†. proceedings of world academy of science, engineering and technology vol 22. 2007.

      [18] R.OGrady. M.Birattari : Fault detection in autonomous robots based on fault injection and learning’ Autonomous robots, 2008.

      [19] Model based fault detection and isolation using locally recurrent neural network. ’lecture notes on computer science vol.5097.2008.

      [20] Yang Z X,Suzuki K.Shimada Y,SayayamaH.Fuzzy: “Fault diagnostic System based on fault tree analysisâ€. IEEE APCCAS.1995:165-170.

      [21] ZiJian Yang Beijing Graduate School.“Survey of modern Fault Diagnosis methods in networks.â€, Chinese Academy of Sciences Beijing,

      [22] Adriana A. Alexandra Research: “Using Expert Systems for Fault Detection and Diagnosis in Industrial Applicationsâ€. Institute for Informatics, Bucharest.

      [23] Yingying Wang ,Qiuju Li, Ming Chang, Hongwei Chen, Guohua Zang: International Conference on Advances in Computational Modelling and Simulation Research on Fault Diagnosis Expert System Based on the Neural Network and the Fault Tree Technology. Procedia Engineering 31 (2012) 1206 – 1210 Elsevier Ltd.doi:10.1016.

      [24] Sylvain Verron, Teodor Tiplica, Abdessamad Kobi: Fault Detection and Diagnosis in a Bayesian Network classifier incorporating probabilistic IFAC-Papers On Line 48-21 670–675., (2015)

      [25] Mohamed Amine Atoui Sylvain Verron Abdessamad Kobi : Fault Detection and Diagnosis in a Bayesian Network classifier incorporating probabilistic boundary : IFAC-Papers On Line 48-21 670–676. (2015).

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    N.S.Kalpana, R., & P.Nallathai, D. (2018). Survey on Fault Detection and Diagnosis Using Neural Network in WBAN. International Journal of Engineering & Technology, 7(2.20), 346-349. https://doi.org/10.14419/ijet.v7i2.20.16731