A Fuzzy Logic-Based Temperature Control System for Baby Incubators

  • Henrikus Pramudia Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia
  • Syaifudin Syaifudin Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia
  • Abd Kholiq Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia
  • Kamilu O. Lawal Department of Electrical and Electronics Engineering, Bauchi, Abubakar Tafawa Balewa University, Nigeria
Keywords: Temperature, Humidity, Fuzzy Logic, Incubator

Abstract

A The purpose of a baby incubator is to help preterm infants whose bodies cannot adapt to their new surroundings by providing them with artificial heat. The goal of this research was to develop a method of applying fuzzy control in conjunction with the DS18B20 sensor for analyzing the response points involved in the construction of a baby incubator. For this experiment, researchers employed conditions of 32 ˚C, 35 ˚C, and 36 ˚C. The Incu analyzer is utilized as the industry standard reference instrument. Fuzzy control on a microcontroller involves a few steps, including fuzzification, which involves inputting the value of the membership function, where this member is a collection of error and feedback values, in this case 0.5; this member is then processed further in fuzzification, which involves transforming raw crisp calculations into membership values via the function membership. A rule base is a set of rules developed to achieve a goal by specifying the appropriate control action in response to a particular input value using linguistic rules. Defuzzification performs calculations of changing fuzzy quantities presented in the form of variable values from the rule base with output values to set an output value that we need in the system. This fuzzy system produces an average rise point of 200 seconds and an overshoot value in the range of +0.50 C. Stability can be achieved within 8 to 10 minutes.

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References

M. Shaib, M. Rashid, L. Hamawy, M. Arnout, I. El Majzoub, and A. J. Zaylaa, “Advanced portable preterm baby incubator,” Int. Conf. Adv. Biomed. Eng. ICABME, vol. 2017-Octob, no. October, 2017, doi: 10.1109/ICABME.2017.8167522.

F. A. Mahapula, K. Kumpuni, J. P. Mlay, and T. F. Mrema, “Risk factors associated with pre-term birth in dar es salaam, tanzania: A case-control study,” Tanzan. J. Health Res., vol. 18, no. 1, pp. 1–8, 2016, doi: 10.4314/thrb.v18i1.4.

M. Ali, M. Abdelwahab, S. Awadekreim, and S. Abdalla, “Development of a Monitoring and Control System of Infant Incubator,” 2018 Int. Conf. Comput. Control. Electr. Electron. Eng. ICCCEEE 2018, no. Lcd, pp. 1–4, 2018, doi: 10.1109/ICCCEEE.2018.8515785.

H. Jadav, A. Bansode, and D. Sharma, “PID Temperature Controller Infant Incubator Using RTD,” IOSR J. Eng. www.iosrjen.org ISSN, vol. 11, p. |Page, 2018.

G. Mathur, “Fuzzy Logic Control For Infant Incubator Systems,” pp. 1–107, 2006.

W. Widhiada, T. G. T. Nindhia, I. Gantara, I. Budarsa, and I. Suarndwipa, “Temperature Stability and Humidity on Infant Incubator Based on Fuzzy Logic Control,” in Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence - ICCAI ’19, 2019, pp. 155–159, doi: 10.1145/3330482.3330527.

L. Nachabe, M. Girod-Genet, B. ElHassan, and J. Jammas, “M-health application for neonatal incubator signals monitoring through a CoAP-based multi-agent system,” 2015 Int. Conf. Adv. Biomed. Eng. ICABME 2015, pp. 170–173, 2015, doi: 10.1109/ICABME.2015.7323279.

N. Y. D. Setyaningsih and A. C. Murti, “Control Temperature on Plant Baby Incubator With Fuzzy Logic,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 7, no. 1, p. 273, 2016, doi: 10.24176/simet.v7i1.514.

A. Latif, H. A. Widodo, R. A. Atmoko, T. N. Phong, and E. T. Helmy, “Temperature and humidity controlling system for baby incubator,” J. Robot. Control, vol. 2, no. 3, pp. 190–193, 2021, doi: 10.18196/jrc.2376.

T. A. Tisa, Z. A. Nisha, and A. Kiber, “DESIGN OF AN ENHANCED TEMPERATURE CONTROL SYSTEM FOR NEONATAL INCUBATOR,” vol. 5, no. 1, pp. 53–62, 2012.

A. V. Zaelani, R. A. Koestoer, I. Roihan, and Harinaldi, “Analysis of temperature stabilization in grashof incubator with environment variations based on Indonesian national standard (SNI),” AIP Conf. Proc., vol. 2062, no. September, 2019, doi: 10.1063/1.5086550.

K. Supriyadi, U. Islam, and S. Agung, “FUZZY LOGIC BASED INCUBATOR TEMP AND HUMID,” vol. 7, no. 3, 2019.

R. Joshi, C. van Pul, L. Atallah, L. Feijs, S. Van Huffel, and P. Andriessen, “Pattern discovery in critical alarms originating from neonates under intensive care.,” Physiol. Meas., vol. 37, no. 4, pp. 564–79, Apr. 2016, doi: 10.1088/0967-3334/37/4/564.

Q. Hidayati, N. Yanti, N. Jamal, and M. Adisaputra, “Portable Baby Incubator Based On Fuzzy Logic,” vol. 8, no. 1, 2020.

S. A. Ili Flores, H. J. Konno, A. M. Massafra, and L. Schiaffino, “Simultaneous Humidity and Temperature Fuzzy Logic Control in Neonatal Incubators,” 2018 Argentine Conf. Autom. Control. AADECA 2018, 2018, doi: 10.23919/AADECA.2018.8577290.

A. Latif, A. Z. Arfianto, J. E. Poetro, T. N. Phong, and E. T. Helmy, “Temperature Monitoring System for Baby Incubator Based on Visual Basic,” vol. 2, no. 1, pp. 47–50, 2021, doi: 10.18196/jrc.2151.

V. C. Kirana, D. H. Andayani, A. Pudji, and A. Hannouch, “Effect of Closed and Opened the Door to Temperature on PID-Based Baby Incubator with Kangaroo Mode,” Indones. J. Electron. Electromed. Eng. Med. informatics, vol. 3, no. 3, pp. 121–127, 2021, doi: 10.35882/ijeeemi.v3i3.6.

N. Azman, I. T. Anggraini, S. R. Wicaksono, and F. Djauhari, “Design of Temperature and Humidity Monitoring Baby Incubator Based on Internet of Things,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 5, pp. 8390–8396, 2020, doi: 10.30534/ijatcse/2020/213952020.

j. E. H. Ali, E. Feki, Z. M.a, C. de prada, and A. Mami, “Incubator System Identification of Humidity an Temperature,” 9Th Int. Renew. Energy Congr., pp. 5–10, 2018.

A. K. Theopaga, A. Rizal, and E. Susanto, “Design and implementation of PID control based baby incubator,” J. Theor. Appl. Inf. Technol., vol. 70, no. 1, pp. 19–24, 2014.

L. Doukkali, F. Z. laamiri, N. B. Mechita, L. Lahlou, M. Habibi, and A. Barkat, “The Issue of Care Given to Premature Infants in the Provincial Hospital Center of Missour,” J. Biosci. Med., vol. 04, no. 05, pp. 76–88, 2016, doi: 10.4236/jbm.2016.45008.

M. U. Cavalcante, B. C. Torrico, O. Da Mota Almeida, A. P. De Souza Braga, and F. L. M. Da Costa Filho, “Filtered model-based predictive control applied to the temperature and humidity control of a neonatal incubator,” 2010 9th IEEE/IAS Int. Conf. Ind. Appl. INDUSCON 2010, no. Figure 1, 2010, doi: 10.1109/INDUSCON.2010.5739884.

A. Alimuddin, R. Arafiyah, I. Saraswati, R. Alfanz, P. Hasudungan, and T. Taufik, “Development and performance study of temperature and humidity regulator in baby incubator using fuzzy-pid hybrid controller,” Energies, vol. 14, no. 20, 2021, doi: 10.3390/en14206505.

W. Widhiada, I. N. G. Antara, I. N. Budiarsa, and I. M. G. Karohika, “The Robust PID Control System of Temperature Stability and Humidity on Infant Incubator Based on Arduino at Mega 2560,” IOP Conf. Ser. Earth Environ. Sci., vol. 248, no. 1, 2019, doi: 10.1088/1755-1315/248/1/012046.

P. Dutta and N. Anjum, “Optimization of Temperature and Relative Humidity in an Automatic Egg Incubator Using Mamdani Fuzzy Inference System,” Int. Conf. Robot. Electr. Signal Process. Tech., pp. 12–16, 2021, doi: 10.1109/ICREST51555.2021.9331155.

Published
2023-11-28
How to Cite
[1]
H. Pramudia, S. Syaifudin, A. Kholiq, and K. O. Lawal, “A Fuzzy Logic-Based Temperature Control System for Baby Incubators”, Indones.J.electronic.electromed.med.inf, vol. 5, no. 4, pp. 232-242, Nov. 2023.
Section
Research Article