آمار

تعداد نشریات11
تعداد شماره‌ها234
تعداد مقالات2,381
تعداد مشاهده مقاله3,802,086
تعداد دریافت فایل اصل مقاله2,763,590
Yektamoghadam, H., Nikoofard, A.. (1399). Fault Detection in Thermoelectric Energy Harvesting of Human Body. فناوری آموزش, 9(1), 57-66. doi: 10.22061/jecei.2020.7392.388
H. Yektamoghadam; A. Nikoofard. "Fault Detection in Thermoelectric Energy Harvesting of Human Body". فناوری آموزش, 9, 1, 1399, 57-66. doi: 10.22061/jecei.2020.7392.388
Yektamoghadam, H., Nikoofard, A.. (1399). 'Fault Detection in Thermoelectric Energy Harvesting of Human Body', فناوری آموزش, 9(1), pp. 57-66. doi: 10.22061/jecei.2020.7392.388
Yektamoghadam, H., Nikoofard, A.. Fault Detection in Thermoelectric Energy Harvesting of Human Body. فناوری آموزش, 1399; 9(1): 57-66. doi: 10.22061/jecei.2020.7392.388

Fault Detection in Thermoelectric Energy Harvesting of Human Body

Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 9، شماره 1، فروردین 2021، صفحه 57-66    اصل مقاله (1.36 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.7392.388
نویسندگان
H. Yektamoghadam؛ A. Nikoofard*
System and Control Department, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
تاریخ دریافت: 18 اردیبهشت 1399،  تاریخ بازنگری: 23 شهریور 1399،  تاریخ پذیرش: 25 آبان 1399 
چکیده
Background and Objective: Human health is an issue that always been a priority for scientists, doctors, medical engineers, and others. A Wireless Body Area Network (WBAN) connects independent nodes (e.g. sensors and actuators) that are situated in the clothes, on the body, or under the skin of a person. In the 21st century, advent the technology in different aspects of human life caused WBAN has a special value in future medical technology. Energy harvesting from the ambient or human body for self-independent from the battery or power supply is an important issue in WBAN. Photovoltaic energy harvesting (PVEH), piezoelectric energy harvesting (PEH), RF energy harvesting (RFEH), and thermal electric energy harvesting (TEH) are some techniques used for energy harvesting in WBAN. Fault detection and diagnosis is an important problem in engineering. Engineers and researchers are always trying to find better ways to identify, detection, and control the fault in different systems.
Methods: We consider a thermal electric generator (TEG) for measurement energy harvested from the human body and power generation on people at different ambient conditions. Also, we used data reduction methods including principle component analysis (PCA), linear discriminant analysis (LDA), and neural network methods including PCA and MLP, LDA and MLP, Dynamic PCA and MLP, Dynamic LDA and MLP to fault detection for thermal electric generator (TEG).
Results: This study shows different data reduction algorithms, in the case studied in this paper, can detect well and nonlinear methods have a more accurate answer than linear methods but implementing the linear methods is easier.
Conclusion: According to simulation results, all the methods discussed in this paper are acceptable for fault detection. In this paper, we introduce data reduction linear and nonlinear algorithms as new methods for fault detection in WBAN.
کلیدواژه‌ها
Wireless Body Area Network؛ Thermal Electric Generator؛ Principle Component Analysis؛ Linear Discriminant Analysis؛ Neural Network
مراجع

[1] A. Liberale, E. Dallago, A. Lazzarini Barnabei, "Energy harvesting system for wireless body sensor nodes," presented at the 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS),  Lausanne, Switzerland, 2014.

[2] A. Mahapatro, P.M. Khilar, "Fault diagnosis in body sensor networks," Int. J. Comput. Inf. Syst. Ind. Manage. Appl., 5: 252-259, 2012.

[3] L. Xiao, R. Li, J. Luo, Z. Xiao, " Energy-efficient recognition of human activity in body sensor networks via compressed classification," Int. J. Distrib. Sens. Netw., 12(12): 1-8, 2016.

[4] P. Sarcevic, Z. Kincses, S. Pletl, "Online human movement classification using wrist-worn wireless sensors," J. Ambient Intell. Hum. Comput., 10: 89–106, 2019.  

[5] R. Gravina, P. Alinia, H. Ghasemzadeh, G. Fortino, "Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges,” Inform. Fusion,  35: 68-80, 2017.

[6] S. Zidi, T. Moulahi, B. Alaya, "Fault detection in wireless sensor networks through SVM classifier," IEEE Sens. J., 18(1): 340-347, 2017.

[7] Z. He et al., "An incipient fault detection approach via detrending and denoising," Control Eng. Pract., 74: 1-12, 2018.

[8] Z. Noshad et al., "Fault detection in wireless sensor networks through the random forest classifier," MDPI, 19: 1-21, 2019.

[9] B. Saneja, R. Rani. 2019. "A scalable correlation‐based approach for outlier detection in wireless body sensor networks," Int. J. Commun. Syst., 32(7): e3918, 2019.

[10] L. Ciabattoni et al.,  "A novel LDA-based approach for motor bearing fault detection," in Proc. 2015 IEEE 13th International Conference on Industrial Informatics (INDIN): 771-776, 2015.

[11] V. Leonov, "Thermoelectric energy harvesting of human body heat for wearable sensors," IEEE Sens. J., 13(6): 2284-2291, 2013.

[12] Y. Wang, Y. Shi, D. Meia, Z. Chen, "Wearable thermoelectric generator to harvest body heat for powering a miniaturized accelerometer," Appl. Energy, 215: 690-698, 2018.

[13] A. Sultana et al., "A pyroelectric generator as a self-powered temperature sensor for sustainable thermal energy harvesting from waste heat and human body heat," Appl. Energy, 221: 299-307, 2018.

[14] J.C. Tudón-Martínez et al., "Comparison of artificial neural networks and dynamic principal component analysis for fault diagnosis," in Proc. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: 10-18, 2011.

[15] H. Yuan, Z. Xiaoxia, Y. Liyang, "A distributed bayesian algorithm for data fault detection in wireless sensor networks," presented at the IEEE International Conference on Information Networking (ICOIN), Cambodia, Cambodia, 2015.

[16] K. Hasan, K. Biswas, K. Ahmed, N.S. Nafi, M. Saiful Islam, "A comprehensive review of wireless body area network," J. Netw. Comput. Appl., 143: 178-198, 2019.

[17] M. Thielen et al., "Human body heat for powering wearable devices: From thermal energy to application," Energy Convers. Manage., 131: 44-54, 2016.

[18] C. Wu et al., "A signal-based fault detection and tolerance control method of current sensor for PMSM drive," IEEE Trans. Ind. Electron., 65(12): 9646 – 9657, 2018.

[19] J. Huang, X. Yan, "Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis, and Bayesian inference,"
Chemometr. Intell. Lab. Syst., 148: 115-127, 2015.

[20] S. Zhoua, J. SinghDhupia, "Online adaptive water management fault diagnosis of PEMFC based on orthogonal linear discriminant analysis and relevance vector machine," Int. J. Hydrogen Energy, 45(11): 7005-7014, 2020.

[21] B. Raheli et al., "Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of langat river," Environ. Earth Sci., 76(14): 1-16, 2017.

[22] M. Saida,G. Zaibi, M. Samet, A. Kachouri, "Improvement of energy harvested from the heat of the human body," presented at the 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia, 2016.

[23] J. Faiz, E. Mazaheri-Tehrani, "Demagnetization modeling and fault diagnosing techniques in permanent magnet machines under stationary and nonstationary conditions: An overview," IEEE Trans. Ind. Appl., 53(3): 2772 – 2785, 2017.

[24] S. Moon, H. Jeong, H. Lee, S.W. Kim, "Detection and classification of demagnetization and interturn short faults of IPMSMs," IEEE Trans. Ind. Electron., 64(12): 9433 - 9441, 2017.

آمار
تعداد مشاهده مقاله: 1,075
تعداد دریافت فایل اصل مقاله: 850


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب