Design and FPGA Implementation of Intelligent Fault Detection in Smart Wireless Sensor Networks

Hadi, Mohammed H.; Issa, Abbas H.; Sabri, Atheer A.

doi:10.30684/etj.v39i4a.1951

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Precision+ Recall (6) where, TP=True Positive (predicted value is positive and actual one is positive). TN=True Negative (predicted value is negative and actual one is negative).…”

Section: F1=2 × Precision× Recallmentioning

confidence: 99%

“…A fault indicates a system malfunction, which leads to unsatisfactory performance and, consequently, the deterioration of the system [4,5]. The output of the system falls between two specific limits (the upper and lower); when one of the two is exceeded, that means there is a failure in the sensor, which is known as a fault [5,6]. In this paper, an intelligent system is designed to monitor the baby incubator's most essential sensors, including temperature and humidity, as well as the fan current and heater current to detect the fault and alert about it.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Mahdi¹,

Gittaffa²,

Issa³

2022

JESA

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The most significant and sensitive component of the biomedical field is the care of newborn newborns. Because of their gestational age or birth weight, certain newborn newborns are at a higher risk of death. Because of their unmet demand for warmth, the majority of preterm newborns born between 32 and 37 weeks of gestation die. The neonatal incubator is a device that provides a regulated and closed environment to preterm newborns. In this paper and depending on technological progress, an intelligent system has been designed to monitor the performance of the incubator sensors depending on four features (temperature, humidity, fan current, and heater current) to detect any fault in the system. The intelligent system employs a low-power computing device to detect the fault in sensors, like the Raspberry Pi 4, which delivers the data from the incubator’s sensors. For classification, tasks adopted many algorithms like Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (NN), then send alerting messages (e-mail, text message) to the nurse or doctor via Wi-Fi. The promising results of the proposed method accuracy come as 98% of DT and SVM while 97.3% of NN.

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“…Precision+ Recall (6) where, TP=True Positive (predicted value is positive and actual one is positive). TN=True Negative (predicted value is negative and actual one is negative).…”

Section: F1=2 × Precision× Recallmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Mahdi¹,

Gittaffa²,

Issa³

2022

JESA

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show abstract

“…On the one hand, in order allows the user to get a high-quality power supply when using sensitive loads, and on the other hand, in order to prevents the user from using high power and nonlinear loads, which cause various power quality problems to be fed back to the grid side. In order to solve the power quality and fault discrimination problems, an online intelligent detection system is designed in this paper, and the application of this system is very helpful in improving the reliability of the power supply [5][6].…”

Section: Introductionmentioning

confidence: 99%

Design of Online Intelligent Detection System for Power Quality and Fault Identification in Distribution Networks

He,

Zhang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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This paper lays the foundation for the subsequent analysis of power quality in distribution networks by briefly describing the indicators of power quality. Utilizing the zero sequence voltage mutation detection fault of a small current routing algorithm, the instantaneous values of voltage and current of each phase of the three-phase circuit are quickly calculated based on the electrical parameters of instantaneous reactive power and the online intelligent detection system is established. The effectiveness and feasibility of the designed detection system and detection algorithm in detecting electrical energy parameters have been verified through experiments. The results show that the error of voltage measurement is within 0.0003-0.002, the frequency error of the system is up to 0.0117%, the measurement error of three-phase unbalance is distributed between 0.001 and 0.1, and the error of negative sequence and zero sequence is between ±0.003, which meet the system design requirements. It shows that the use of online intelligent detection systems can improve the quality of distribution network operation and fault identification accuracy, improve power quality, and effectively improve the reliability of power supply.

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Improved UAV blade unbalance prediction based on machine learning and ReliefF supreme feature ranking method

Al-Haddad,

Jaber

2023

J Braz. Soc. Mech. Sci. Eng.

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Design and FPGA Implementation of Intelligent Fault Detection in Smart Wireless Sensor Networks

Cited by 4 publications

References 17 publications

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Design of Online Intelligent Detection System for Power Quality and Fault Identification in Distribution Networks

Improved UAV blade unbalance prediction based on machine learning and ReliefF supreme feature ranking method

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