Mitigation of hotspots in electrical components and equipment using an adaptive neuro-fuzzy inference system

Oluseyi, P.O.; Adeagbo, J. A.; Dinakin, Demilade D.; Akinbulire, Tolu O.

doi:10.1007/s00202-020-01028-0

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…is model processes information through a large number of neurons with nonlinear mapping ability. In the network, neurons are organized in the form of hierarchical structure [15][16][17][18], and the processing units on each layer are connected with neurons on other layers in a weighted way. Using error back propagation, a three-layer forward neural network model is obtained, with the specific structure as shown in Figure 1.…”

Section: Design Of Fault Diagnosis Structure Of Electrical Equipment ...mentioning

confidence: 99%

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis

Zhang

Lyu

2022

Computational Intelligence and Neuroscience

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In order to improve the accuracy of electrical equipment failure diagnosis and keep electrical equipment operating safely and efficiently, this paper proposes to design an electrical equipment failure diagnosis system based on a neural network, analyze the faults of electrical equipment and their causes, and establish knowledge base according to relevant data and expert judgment. The fault knowledge base was introduced into the neural network operation structure, and the fault diagnosis results were classified step by step through multiple subnetworks. In data preprocessing, in order to avoid the redundancy of primary fault information features, the principal component heuristic attribute reduction algorithm was used to select the fault data samples optimally. The neural network learning algorithm is used to calculate the forward direction and error rate of the initial error data, and the reliability function is used to optimize the initial weight threshold of the neural network, propagating the error backwards and high. Experimental results show that adding attribute reduction improves error classification performance, avoids the problem of local minima through neural network operation, and has fewer iteration steps, lower average error, and higher accuracy of fault diagnosis, reaching 95.6%.

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Section: Design Of Fault Diagnosis Structure Of Electrical Equipment ...mentioning

confidence: 99%

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis

Zhang

Lyu

2022

Computational Intelligence and Neuroscience

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“…Also, when inflammable thrash is thrown away improperly and comes in contact with an ignition source, it can lead to fire. According to Adekunle et al [22], fire is caused in buildings as a result of improper wiring and faulty installation of electrical equipment. Anaglatey [23] also observed that fire in institutional buildings result from poor electrical designs and illegal connections to other sources of electricity.…”

Section: Figure 1 the Fire Triadmentioning

confidence: 99%

Fire Emergency Safety Preparedness in the College of Leadership Development Studies Building in Covenant University, Ota, Nigeria

Babatunde¹,

Oche²,

Paul³

2020

cea

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Fire outbreaks often occur with the exigency of appropriate response from building occupants in order to safely escape it. Fire safety in buildings is a major design consideration in architecture which helps everyone to escape by way of the most harmless and closest exit point. This research is a case study of the fire emergency safety preparedness in the College of Leadership Development Studies building in Covenant University, Ota in Nigeria, to establish how prepared the building and its users are towards combating a fire outbreak, with a view to identify areas for further improvements. The study adopted a pragmatic research approach that made use of both qualitative and quantitative research methods. The results revealed that the fire emergency safety preparedness of the college building is low. The active and passive fire emergency safety provisions of the building, exhibited various levels of inadequacies. A significant finding was that there was no provision of any full scale firefighting devices in the building. Also, while most of the respondents can easily locate the active emergency firefighting hand appliances situated in the building, majority of them cannot operate the devices. Fire drills were also found not to be a regular periodic exercise in the building. Some of the recommendations made are that: the building should be retrofitted with the various active and passive fire emergency safety measures found to be lacking; also workshops, seminars and fire drills should be conducted periodically in conformity with best practices.

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Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

Ukiwe,

Adeshina,

Jacob

et al. 2024

Journal of Electrical Systems and Inf Technol

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Hotspots in electrical power equipment or installations are a major issue whenever it occurs within the power system. Factors responsible for this phenomenon are many, sometimes inter-related and other times they are isolated. Electrical hotspots caused by poor connections are common. Deep learning models have become popular for diagnosing anomalies in physical and biological systems, by the instrumentality of feature extraction of images in convolutional neural networks. In this work, a VGG-16 deep neural network model is applied for identifying electrical hotspots by means of transfer learning. This model was achieved by first augmenting the acquired infrared thermographic images, using the pre-trained ImageNet weights of the VGG-16 algorithm with additional global average pooling in place of conventional fully connected layers and a softmax layer at the output. With the categorical cross-entropy loss function, the model was implemented using the Adam optimizer at learning rate of 0.0001 as well as some variants of the Adam optimization algorithm. On evaluation, with a test IRT image dataset, and a comparison with similar works, the research showed that a better accuracy of 99.98% in identification of electrical hotspots was achieved. The model shows good score in performance metrics like accuracy, precision, recall, and F1-score. The obtained results proved the potential of deep learning using computer vision parameters for infrared thermographic identification of electrical hotspots in power system installations. Also, there is need for careful selection of the IR sensor’s thermal range during image acquisition, and suitable choice of color palette would make for easy hotspot isolation, reduce the pixel to pixel temperature differential across any of the images, and easily highlight the critical region of interest with high pixel values. However, it makes edge detection difficult for human visual perception which computer vision-based deep learning model could overcome.

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Mitigation of hotspots in electrical components and equipment using an adaptive neuro-fuzzy inference system

Cited by 3 publications

References 16 publications

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis

Fire Emergency Safety Preparedness in the College of Leadership Development Studies Building in Covenant University, Ota, Nigeria

Deep learning model for detection of hotspots using infrared thermographic images of electrical installations

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