Abstract
When an electrical machine operates with load current that does not exceed its current rating, the temperature rise of any part will never exceed the permissible limits, thus providing continuous and reliable operation. IoT-based protection system is proposed for the identification of any runtime current/voltage conditions that leads to breakdown. IoT implementation with sensor data analysis in using artificial neural networks and cloud computing is presented in this paper. Here, we use LoRa network to transmit the hall effect sensor with raw data that measures current to another node which connects with the system. In the system, we use C# program to process the raw data and extract meaning from it using artificial neural networks that learn to filter the raw data. The data is then sent to the cloud server. The simulation shows that we have to maintain a threshold condition to improve the quality of the electrical machine.
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References
Y.H. Ali, M.H Omar, R.A Rahman, Acoustic emission technique in condition monitoring and fault diagnosis of gears and bearings. Int. J. Acad. Res. 6(5) 2014
Y.H. Ali, R.A. Rahmen, R. Hamzah, Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis. Rev. J. Tecnol. 69(2) (2014)
R. Zhang, Z. Peng, L. Wu, B. Yao, Y. Guan, Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors 17(549) (2017)
G. Xu, M. Liu, Z. Jiang, D. Soffker, W. Shen, Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning. Sensors 19(5) (2019)
Y.H. Ali, Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis (Intech Open, 2018), p. 74932, Chapter 14
P.R. Aguiar, C.H. Martins, M. Marchiand, E.C. Bianchi, Digital Signal Processing for Acoustic Emission (INTECH, 2012), Chapter 12
F. Sheikh, X. Li, Wireless sensor network system design using Raspberry Pi and Arduino for environmental monitoring applications. Procedia Comput. Sci. 34, 103–110 (2014)
A. Zaslavsky, C. Perera, D. Georgakopoulos, Sensing as a service and big data, in Proceedings of the International Conference on Advances in Cloud Computing (ACC), Bangalore, India, July 2012
G. Jayawardhana, R. Buyyab, S. Marusic, M. Palaniswami, Internet of things (IoT): a vision, architectural elements and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
A. Augustin, J. Yi, T. Clausen, W.M. Townsley, A study of loRa: long range and low power networks for the internet of things. Sensors (2016)
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Rath, A., Mishra, D.K., Baig, S.Q., Devi, G. (2021). Frequency Detection and Variation with Smart-Sensor Data Analysis Using Artificial Neural Network and Cloud Computing. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_14
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DOI: https://doi.org/10.1007/978-981-15-7106-0_14
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